Overview

Dataset statistics

Number of variables55
Number of observations42350
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory75.3 MiB
Average record size in memory1.8 KiB

Variable types

Numeric28
Categorical26
Boolean1

Warnings

int_rate has a high cardinality: 394 distinct values High cardinality
emp_title has a high cardinality: 30537 distinct values High cardinality
issue_d has a high cardinality: 55 distinct values High cardinality
desc has a high cardinality: 28833 distinct values High cardinality
title has a high cardinality: 21173 distinct values High cardinality
zip_code has a high cardinality: 836 distinct values High cardinality
earliest_cr_line has a high cardinality: 530 distinct values High cardinality
revol_util has a high cardinality: 1119 distinct values High cardinality
last_pymnt_d has a high cardinality: 112 distinct values High cardinality
last_credit_pull_d has a high cardinality: 133 distinct values High cardinality
loan_amnt is highly correlated with funded_amnt and 2 other fieldsHigh correlation
funded_amnt is highly correlated with loan_amnt and 3 other fieldsHigh correlation
funded_amnt_inv is highly correlated with loan_amnt and 2 other fieldsHigh correlation
installment is highly correlated with loan_amnt and 1 other fieldsHigh correlation
fico_range_low is highly correlated with fico_range_highHigh correlation
fico_range_high is highly correlated with fico_range_lowHigh correlation
total_pymnt is highly correlated with funded_amnt and 2 other fieldsHigh correlation
total_pymnt_inv is highly correlated with funded_amnt_inv and 2 other fieldsHigh correlation
total_rec_prncp is highly correlated with total_pymnt and 1 other fieldsHigh correlation
grade_new is highly correlated with int_rate_newHigh correlation
int_rate_new is highly correlated with grade_newHigh correlation
grade is highly correlated with sub_gradeHigh correlation
term_new is highly correlated with termHigh correlation
home_ownership_new is highly correlated with home_ownershipHigh correlation
loan_status is highly correlated with loan_status_newHigh correlation
home_ownership is highly correlated with home_ownership_newHigh correlation
sub_grade is highly correlated with gradeHigh correlation
verification_status_new is highly correlated with verification_statusHigh correlation
loan_status_new is highly correlated with loan_statusHigh correlation
term is highly correlated with term_newHigh correlation
verification_status is highly correlated with verification_status_newHigh correlation
annual_inc is highly skewed (γ1 = 29.09932812) Skewed
collection_recovery_fee is highly skewed (γ1 = 22.55504187) Skewed
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique
delinq_2yrs has 37632 (88.9%) zeros Zeros
inq_last_6mths has 19606 (46.3%) zeros Zeros
pub_rec has 39984 (94.4%) zeros Zeros
revol_bal has 1028 (2.4%) zeros Zeros
total_rec_late_fee has 39972 (94.4%) zeros Zeros
recoveries has 36077 (85.2%) zeros Zeros
collection_recovery_fee has 38100 (90.0%) zeros Zeros
last_fico_range_low has 773 (1.8%) zeros Zeros
emp_length_new has 5017 (11.8%) zeros Zeros

Reproduction

Analysis started2021-03-22 12:17:23.949546
Analysis finished2021-03-22 12:19:34.403952
Duration2 minutes and 10.45 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct42350
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21230.35955
Minimum0
Maximum42514
Zeros1
Zeros (%)< 0.1%
Memory size331.0 KiB
2021-03-22T13:19:34.489950image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2118.45
Q110606.25
median21220.5
Q331846.75
95-th percentile40364.55
Maximum42514
Range42514
Interquartile range (IQR)21240.5

Descriptive statistics

Standard deviation12266.4043
Coefficient of variation (CV)0.577776569
Kurtosis-1.199107321
Mean21230.35955
Median Absolute Deviation (MAD)10620.5
Skewness0.001894126477
Sum899105727
Variance150464674.5
MonotocityStrictly increasing
2021-03-22T13:19:34.618944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20471
 
< 0.1%
361871
 
< 0.1%
402811
 
< 0.1%
382321
 
< 0.1%
115991
 
< 0.1%
95501
 
< 0.1%
156931
 
< 0.1%
136441
 
< 0.1%
34031
 
< 0.1%
13541
 
< 0.1%
Other values (42340)42340
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
425141
< 0.1%
425131
< 0.1%
425121
< 0.1%
425111
< 0.1%
425081
< 0.1%
425071
< 0.1%
425061
< 0.1%
425051
< 0.1%
425041
< 0.1%
425031
< 0.1%

loan_amnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct897
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11109.21192
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Memory size331.0 KiB
2021-03-22T13:19:34.751940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2400
Q15200
median9800
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9800

Descriptive statistics

Standard deviation7409.408311
Coefficient of variation (CV)0.6669607495
Kurtosis0.7839741968
Mean11109.21192
Median Absolute Deviation (MAD)4800
Skewness1.063773126
Sum470475125
Variance54899331.52
MonotocityNot monotonic
2021-03-22T13:19:34.877936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100003008
 
7.1%
120002436
 
5.8%
50002246
 
5.3%
60002033
 
4.8%
150002010
 
4.7%
200001717
 
4.1%
80001690
 
4.0%
250001495
 
3.5%
40001224
 
2.9%
30001123
 
2.7%
Other values (887)23368
55.2%
ValueCountFrequency (%)
50011
< 0.1%
5501
 
< 0.1%
6006
< 0.1%
7002
 
< 0.1%
7251
 
< 0.1%
7501
 
< 0.1%
8003
 
< 0.1%
8501
 
< 0.1%
9004
 
< 0.1%
9251
 
< 0.1%
ValueCountFrequency (%)
35000684
1.6%
348002
 
< 0.1%
346751
 
< 0.1%
345251
 
< 0.1%
344755
 
< 0.1%
342001
 
< 0.1%
3400015
 
< 0.1%
339509
 
< 0.1%
336006
 
< 0.1%
335002
 
< 0.1%

funded_amnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1050
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10840.21133
Minimum500
Maximum35000
Zeros0
Zeros (%)0.0%
Memory size331.0 KiB
2021-03-22T13:19:35.018931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile2361.25
Q15100
median9600
Q315000
95-th percentile25000
Maximum35000
Range34500
Interquartile range (IQR)9900

Descriptive statistics

Standard deviation7145.056871
Coefficient of variation (CV)0.6591252376
Kurtosis0.9465127899
Mean10840.21133
Median Absolute Deviation (MAD)4600
Skewness1.084671048
Sum459082950
Variance51051837.7
MonotocityNot monotonic
2021-03-22T13:19:35.148942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100002916
 
6.9%
120002344
 
5.5%
50002233
 
5.3%
60002019
 
4.8%
150001895
 
4.5%
80001677
 
4.0%
200001539
 
3.6%
40001224
 
2.9%
250001220
 
2.9%
30001114
 
2.6%
Other values (1040)24169
57.1%
ValueCountFrequency (%)
50011
< 0.1%
5501
 
< 0.1%
6006
< 0.1%
7002
 
< 0.1%
7251
 
< 0.1%
7501
 
< 0.1%
8003
 
< 0.1%
8501
 
< 0.1%
9004
 
< 0.1%
9251
 
< 0.1%
ValueCountFrequency (%)
35000558
1.3%
348001
 
< 0.1%
346752
 
< 0.1%
345251
 
< 0.1%
344754
 
< 0.1%
342501
 
< 0.1%
3400014
 
< 0.1%
339506
 
< 0.1%
336006
 
< 0.1%
335001
 
< 0.1%

funded_amnt_inv
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9230
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10162.36456
Minimum0
Maximum35000
Zeros224
Zeros (%)0.5%
Memory size331.0 KiB
2021-03-22T13:19:35.291932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1500
Q14964.289886
median8500
Q314000
95-th percentile24600
Maximum35000
Range35000
Interquartile range (IQR)9035.710114

Descriptive statistics

Standard deviation7129.98979
Coefficient of variation (CV)0.7016073619
Kurtosis1.06729035
Mean10162.36456
Median Absolute Deviation (MAD)4200
Skewness1.103832594
Sum430376139.1
Variance50836754.41
MonotocityNot monotonic
2021-03-22T13:19:35.417928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50001366
 
3.2%
100001300
 
3.1%
60001240
 
2.9%
120001084
 
2.6%
8000924
 
2.2%
4000848
 
2.0%
3000846
 
2.0%
15000664
 
1.6%
7000613
 
1.4%
2000479
 
1.1%
Other values (9220)32986
77.9%
ValueCountFrequency (%)
0224
0.5%
0.0001210981081
 
< 0.1%
0.0001853694011
 
< 0.1%
0.0005311330691
 
< 0.1%
0.0005717830381
 
< 0.1%
0.0006546069551
 
< 0.1%
0.000899697281
 
< 0.1%
0.0009220301071
 
< 0.1%
0.0010836096771
 
< 0.1%
0.001123457451
 
< 0.1%
ValueCountFrequency (%)
35000135
0.3%
34997.352451
 
< 0.1%
34993.655391
 
< 0.1%
34993.325711
 
< 0.1%
34993.263061
 
< 0.1%
34993.196961
 
< 0.1%
34990.43081
 
< 0.1%
34987.984521
 
< 0.1%
34987.271011
 
< 0.1%
34977.346741
 
< 0.1%

term
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
36 months
31374 
60 months
10976 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters423500
Distinct characters10
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 36 months
2nd row 60 months
3rd row 36 months
4th row 36 months
5th row 60 months
ValueCountFrequency (%)
36 months31374
74.1%
60 months10976
 
25.9%
2021-03-22T13:19:35.664911image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-22T13:19:35.739917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
months42350
50.0%
3631374
37.0%
6010976
 
13.0%

Most occurring characters

ValueCountFrequency (%)
84700
20.0%
642350
10.0%
m42350
10.0%
o42350
10.0%
n42350
10.0%
t42350
10.0%
h42350
10.0%
s42350
10.0%
331374
 
7.4%
010976
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter254100
60.0%
Space Separator84700
 
20.0%
Decimal Number84700
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
m42350
16.7%
o42350
16.7%
n42350
16.7%
t42350
16.7%
h42350
16.7%
s42350
16.7%
ValueCountFrequency (%)
642350
50.0%
331374
37.0%
010976
 
13.0%
ValueCountFrequency (%)
84700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin254100
60.0%
Common169400
40.0%

Most frequent character per script

ValueCountFrequency (%)
m42350
16.7%
o42350
16.7%
n42350
16.7%
t42350
16.7%
h42350
16.7%
s42350
16.7%
ValueCountFrequency (%)
84700
50.0%
642350
25.0%
331374
 
18.5%
010976
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII423500
100.0%

Most frequent character per block

ValueCountFrequency (%)
84700
20.0%
642350
10.0%
m42350
10.0%
o42350
10.0%
n42350
10.0%
t42350
10.0%
h42350
10.0%
s42350
10.0%
331374
 
7.4%
010976
 
2.6%

int_rate
Categorical

HIGH CARDINALITY

Distinct394
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
10.99%
 
970
11.49%
 
831
13.49%
 
830
7.51%
 
786
7.88%
 
742
Other values (389)
38191 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters296450
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st row 10.65%
2nd row 15.27%
3rd row 15.96%
4th row 13.49%
5th row 12.69%
ValueCountFrequency (%)
10.99%970
 
2.3%
11.49%831
 
2.0%
13.49%830
 
2.0%
7.51%786
 
1.9%
7.88%742
 
1.8%
7.49%656
 
1.5%
11.71%609
 
1.4%
9.99%607
 
1.4%
7.90%582
 
1.4%
5.42%573
 
1.4%
Other values (384)35164
83.0%
2021-03-22T13:19:35.969900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10.99970
 
2.3%
11.49831
 
2.0%
13.49830
 
2.0%
7.51786
 
1.9%
7.88742
 
1.8%
7.49656
 
1.5%
11.71609
 
1.4%
9.99607
 
1.4%
7.90582
 
1.4%
5.42573
 
1.4%
Other values (384)35164
83.0%

Most occurring characters

ValueCountFrequency (%)
54660
18.4%
.42350
14.3%
%42350
14.3%
141385
14.0%
922572
7.6%
213665
 
4.6%
612819
 
4.3%
712789
 
4.3%
411962
 
4.0%
310890
 
3.7%
Other values (3)31008
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number157090
53.0%
Other Punctuation84700
28.6%
Space Separator54660
 
18.4%

Most frequent character per category

ValueCountFrequency (%)
141385
26.3%
922572
14.4%
213665
 
8.7%
612819
 
8.2%
712789
 
8.1%
411962
 
7.6%
310890
 
6.9%
510860
 
6.9%
810290
 
6.6%
09858
 
6.3%
ValueCountFrequency (%)
.42350
50.0%
%42350
50.0%
ValueCountFrequency (%)
54660
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common296450
100.0%

Most frequent character per script

ValueCountFrequency (%)
54660
18.4%
.42350
14.3%
%42350
14.3%
141385
14.0%
922572
7.6%
213665
 
4.6%
612819
 
4.3%
712789
 
4.3%
411962
 
4.0%
310890
 
3.7%
Other values (3)31008
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII296450
100.0%

Most frequent character per block

ValueCountFrequency (%)
54660
18.4%
.42350
14.3%
%42350
14.3%
141385
14.0%
922572
7.6%
213665
 
4.6%
612819
 
4.3%
712789
 
4.3%
411962
 
4.0%
310890
 
3.7%
Other values (3)31008
10.5%

installment
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16398
Distinct (%)38.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean323.124979
Minimum15.67
Maximum1305.19
Zeros0
Zeros (%)0.0%
Memory size331.0 KiB
2021-03-22T13:19:36.086897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum15.67
5-th percentile70.0335
Q1165.74
median278.41
Q3428.9075
95-th percentile762.08
Maximum1305.19
Range1289.52
Interquartile range (IQR)263.1675

Descriptive statistics

Standard deviation208.8720333
Coefficient of variation (CV)0.6464125241
Kurtosis1.204866192
Mean323.124979
Median Absolute Deviation (MAD)122.66
Skewness1.124462048
Sum13684342.86
Variance43627.52628
MonotocityNot monotonic
2021-03-22T13:19:36.219892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311.1168
 
0.2%
180.9659
 
0.1%
311.0254
 
0.1%
150.848
 
0.1%
368.4546
 
0.1%
372.1245
 
0.1%
330.7643
 
0.1%
339.3142
 
0.1%
317.7242
 
0.1%
186.6141
 
0.1%
Other values (16388)41862
98.8%
ValueCountFrequency (%)
15.671
< 0.1%
15.691
< 0.1%
15.751
< 0.1%
15.761
< 0.1%
15.911
< 0.1%
16.081
< 0.1%
16.251
< 0.1%
16.311
< 0.1%
16.471
< 0.1%
16.731
< 0.1%
ValueCountFrequency (%)
1305.191
 
< 0.1%
1302.691
 
< 0.1%
1295.211
 
< 0.1%
1288.12
 
< 0.1%
1283.51
 
< 0.1%
1276.63
< 0.1%
1272.21
 
< 0.1%
1269.735
< 0.1%
1265.161
 
< 0.1%
1263.231
 
< 0.1%

grade
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
B
12355 
A
10163 
C
8689 
D
5975 
E
3369 
Other values (2)
1799 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters42350
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowC
3rd rowC
4th rowC
5th rowB
ValueCountFrequency (%)
B12355
29.2%
A10163
24.0%
C8689
20.5%
D5975
14.1%
E3369
 
8.0%
F1292
 
3.1%
G507
 
1.2%
2021-03-22T13:19:36.479886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-22T13:19:36.561882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
b12355
29.2%
a10163
24.0%
c8689
20.5%
d5975
14.1%
e3369
 
8.0%
f1292
 
3.1%
g507
 
1.2%

Most occurring characters

ValueCountFrequency (%)
B12355
29.2%
A10163
24.0%
C8689
20.5%
D5975
14.1%
E3369
 
8.0%
F1292
 
3.1%
G507
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter42350
100.0%

Most frequent character per category

ValueCountFrequency (%)
B12355
29.2%
A10163
24.0%
C8689
20.5%
D5975
14.1%
E3369
 
8.0%
F1292
 
3.1%
G507
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
Latin42350
100.0%

Most frequent character per script

ValueCountFrequency (%)
B12355
29.2%
A10163
24.0%
C8689
20.5%
D5975
14.1%
E3369
 
8.0%
F1292
 
3.1%
G507
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII42350
100.0%

Most frequent character per block

ValueCountFrequency (%)
B12355
29.2%
A10163
24.0%
C8689
20.5%
D5975
14.1%
E3369
 
8.0%
F1292
 
3.1%
G507
 
1.2%

sub_grade
Categorical

HIGH CORRELATION

Distinct35
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
B3
2992 
A4
2904 
B5
 
2799
A5
 
2786
B4
 
2578
Other values (30)
28291 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters84700
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB2
2nd rowC4
3rd rowC5
4th rowC1
5th rowB5
ValueCountFrequency (%)
B32992
 
7.1%
A42904
 
6.9%
B52799
 
6.6%
A52786
 
6.6%
B42578
 
6.1%
C12256
 
5.3%
C22143
 
5.1%
B22107
 
5.0%
B11879
 
4.4%
A31817
 
4.3%
Other values (25)18089
42.7%
2021-03-22T13:19:36.845874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b32992
 
7.1%
a42904
 
6.9%
b52799
 
6.6%
a52786
 
6.6%
b42578
 
6.1%
c12256
 
5.3%
c22143
 
5.1%
b22107
 
5.0%
b11879
 
4.4%
a31817
 
4.3%
Other values (25)18089
42.7%

Most occurring characters

ValueCountFrequency (%)
B12355
14.6%
A10163
12.0%
48833
10.4%
38744
10.3%
C8689
10.3%
58605
10.2%
28440
10.0%
17728
9.1%
D5975
7.1%
E3369
 
4.0%
Other values (2)1799
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter42350
50.0%
Decimal Number42350
50.0%

Most frequent character per category

ValueCountFrequency (%)
B12355
29.2%
A10163
24.0%
C8689
20.5%
D5975
14.1%
E3369
 
8.0%
F1292
 
3.1%
G507
 
1.2%
ValueCountFrequency (%)
48833
20.9%
38744
20.6%
58605
20.3%
28440
19.9%
17728
18.2%

Most occurring scripts

ValueCountFrequency (%)
Latin42350
50.0%
Common42350
50.0%

Most frequent character per script

ValueCountFrequency (%)
B12355
29.2%
A10163
24.0%
C8689
20.5%
D5975
14.1%
E3369
 
8.0%
F1292
 
3.1%
G507
 
1.2%
ValueCountFrequency (%)
48833
20.9%
38744
20.6%
58605
20.3%
28440
19.9%
17728
18.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII84700
100.0%

Most frequent character per block

ValueCountFrequency (%)
B12355
14.6%
A10163
12.0%
48833
10.4%
38744
10.3%
C8689
10.3%
58605
10.2%
28440
10.0%
17728
9.1%
D5975
7.1%
E3369
 
4.0%
Other values (2)1799
 
2.1%

emp_title
Categorical

HIGH CARDINALITY

Distinct30537
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
US Army
 
2735
Bank of America
 
115
IBM
 
72
AT&T
 
61
Kaiser Permanente
 
61
Other values (30532)
39306 

Length

Max length78
Median length17
Mean length17.65227863
Min length2

Characters and Unicode

Total characters747574
Distinct characters96
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27076 ?
Unique (%)63.9%

Sample

1st rowUS Army
2nd rowRyder
3rd rowUS Army
4th rowAIR RESOURCES BOARD
5th rowUniversity Medical Group
ValueCountFrequency (%)
US Army2735
 
6.5%
Bank of America115
 
0.3%
IBM72
 
0.2%
AT&T61
 
0.1%
Kaiser Permanente61
 
0.1%
UPS57
 
0.1%
Wells Fargo57
 
0.1%
USAF56
 
0.1%
US Air Force55
 
0.1%
Self Employed49
 
0.1%
Other values (30527)39032
92.2%
2021-03-22T13:19:37.179861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc3393
 
3.0%
us3291
 
2.9%
of3194
 
2.8%
army3035
 
2.7%
1275
 
1.1%
and1033
 
0.9%
services866
 
0.8%
center863
 
0.8%
bank848
 
0.8%
county844
 
0.8%
Other values (19677)93729
83.4%

Most occurring characters

ValueCountFrequency (%)
71540
 
9.6%
e59721
 
8.0%
a46624
 
6.2%
n45427
 
6.1%
r45330
 
6.1%
o45298
 
6.1%
i43084
 
5.8%
t41044
 
5.5%
s32366
 
4.3%
l27583
 
3.7%
Other values (86)289557
38.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter529016
70.8%
Uppercase Letter135179
 
18.1%
Space Separator71540
 
9.6%
Other Punctuation9348
 
1.3%
Dash Punctuation1093
 
0.1%
Decimal Number1021
 
0.1%
Open Punctuation172
 
< 0.1%
Close Punctuation169
 
< 0.1%
Math Symbol23
 
< 0.1%
Currency Symbol3
 
< 0.1%
Other values (5)10
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
S16791
 
12.4%
C15492
 
11.5%
A12061
 
8.9%
I8053
 
6.0%
M6931
 
5.1%
U6626
 
4.9%
P6445
 
4.8%
T6103
 
4.5%
L5959
 
4.4%
E5624
 
4.2%
Other values (18)45094
33.4%
ValueCountFrequency (%)
e59721
11.3%
a46624
 
8.8%
n45427
 
8.6%
r45330
 
8.6%
o45298
 
8.6%
i43084
 
8.1%
t41044
 
7.8%
s32366
 
6.1%
l27583
 
5.2%
c24612
 
4.7%
Other values (17)117927
22.3%
ValueCountFrequency (%)
.4534
48.5%
,2334
25.0%
&1373
 
14.7%
'685
 
7.3%
/333
 
3.6%
#37
 
0.4%
@10
 
0.1%
!9
 
0.1%
:9
 
0.1%
"8
 
0.1%
Other values (5)16
 
0.2%
ValueCountFrequency (%)
1202
19.8%
2170
16.7%
3161
15.8%
0105
10.3%
499
9.7%
575
 
7.3%
967
 
6.6%
661
 
6.0%
746
 
4.5%
835
 
3.4%
ValueCountFrequency (%)
+20
87.0%
|2
 
8.7%
<1
 
4.3%
ValueCountFrequency (%)
(171
99.4%
[1
 
0.6%
ValueCountFrequency (%)
€1
50.0%
ƒ1
50.0%
ValueCountFrequency (%)
$2
66.7%
¢1
33.3%
ValueCountFrequency (%)
71540
100.0%
ValueCountFrequency (%)
-1093
100.0%
ValueCountFrequency (%)
)169
100.0%
ValueCountFrequency (%)
©2
100.0%
ValueCountFrequency (%)
`2
100.0%
ValueCountFrequency (%)
_2
100.0%
ValueCountFrequency (%)
²2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin664195
88.8%
Common83379
 
11.2%

Most frequent character per script

ValueCountFrequency (%)
e59721
 
9.0%
a46624
 
7.0%
n45427
 
6.8%
r45330
 
6.8%
o45298
 
6.8%
i43084
 
6.5%
t41044
 
6.2%
s32366
 
4.9%
l27583
 
4.2%
c24612
 
3.7%
Other values (45)253106
38.1%
ValueCountFrequency (%)
71540
85.8%
.4534
 
5.4%
,2334
 
2.8%
&1373
 
1.6%
-1093
 
1.3%
'685
 
0.8%
/333
 
0.4%
1202
 
0.2%
(171
 
0.2%
2170
 
0.2%
Other values (31)944
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII747560
> 99.9%
None14
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
71540
 
9.6%
e59721
 
8.0%
a46624
 
6.2%
n45427
 
6.1%
r45330
 
6.1%
o45298
 
6.1%
i43084
 
5.8%
t41044
 
5.5%
s32366
 
4.3%
l27583
 
3.7%
Other values (77)289543
38.7%
ValueCountFrequency (%)
Ã3
21.4%
©2
14.3%
Â2
14.3%
²2
14.3%
â1
 
7.1%
€1
 
7.1%
¢1
 
7.1%
ƒ1
 
7.1%
¡1
 
7.1%

emp_length
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
10+ years
10456 
< 1 year
5017 
2 years
4728 
3 years
4351 
4 years
3631 
Other values (6)
14167 

Length

Max length9
Median length7
Mean length7.527933884
Min length6

Characters and Unicode

Total characters318808
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10+ years
2nd row< 1 year
3rd row10+ years
4th row10+ years
5th row1 year
ValueCountFrequency (%)
10+ years10456
24.7%
< 1 year5017
11.8%
2 years4728
11.2%
3 years4351
10.3%
4 years3631
 
8.6%
1 year3571
 
8.4%
5 years3441
 
8.1%
6 years2366
 
5.6%
7 years1870
 
4.4%
8 years1585
 
3.7%
2021-03-22T13:19:37.423853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
years33762
37.6%
1010456
 
11.7%
year8588
 
9.6%
18588
 
9.6%
5017
 
5.6%
24728
 
5.3%
34351
 
4.8%
43631
 
4.0%
53441
 
3.8%
62366
 
2.6%
Other values (3)4789
 
5.3%

Most occurring characters

ValueCountFrequency (%)
47367
14.9%
y42350
13.3%
e42350
13.3%
a42350
13.3%
r42350
13.3%
s33762
10.6%
119044
6.0%
010456
 
3.3%
+10456
 
3.3%
<5017
 
1.6%
Other values (8)23306
7.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter203162
63.7%
Decimal Number52806
 
16.6%
Space Separator47367
 
14.9%
Math Symbol15473
 
4.9%

Most frequent character per category

ValueCountFrequency (%)
119044
36.1%
010456
19.8%
24728
 
9.0%
34351
 
8.2%
43631
 
6.9%
53441
 
6.5%
62366
 
4.5%
71870
 
3.5%
81585
 
3.0%
91334
 
2.5%
ValueCountFrequency (%)
y42350
20.8%
e42350
20.8%
a42350
20.8%
r42350
20.8%
s33762
16.6%
ValueCountFrequency (%)
+10456
67.6%
<5017
32.4%
ValueCountFrequency (%)
47367
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin203162
63.7%
Common115646
36.3%

Most frequent character per script

ValueCountFrequency (%)
47367
41.0%
119044
16.5%
010456
 
9.0%
+10456
 
9.0%
<5017
 
4.3%
24728
 
4.1%
34351
 
3.8%
43631
 
3.1%
53441
 
3.0%
62366
 
2.0%
Other values (3)4789
 
4.1%
ValueCountFrequency (%)
y42350
20.8%
e42350
20.8%
a42350
20.8%
r42350
20.8%
s33762
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII318808
100.0%

Most frequent character per block

ValueCountFrequency (%)
47367
14.9%
y42350
13.3%
e42350
13.3%
a42350
13.3%
r42350
13.3%
s33762
10.6%
119044
6.0%
010456
 
3.3%
+10456
 
3.3%
<5017
 
1.6%
Other values (8)23306
7.3%

home_ownership
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
RENT
20060 
MORTGAGE
18917 
OWN
3235 
OTHER
 
134
NONE
 
4

Length

Max length8
Median length4
Mean length5.713506494
Min length3

Characters and Unicode

Total characters241967
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRENT
2nd rowRENT
3rd rowRENT
4th rowRENT
5th rowRENT
ValueCountFrequency (%)
RENT20060
47.4%
MORTGAGE18917
44.7%
OWN3235
 
7.6%
OTHER134
 
0.3%
NONE4
 
< 0.1%
2021-03-22T13:19:37.652846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-22T13:19:37.722844image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
rent20060
47.4%
mortgage18917
44.7%
own3235
 
7.6%
other134
 
0.3%
none4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
E39115
16.2%
R39111
16.2%
T39111
16.2%
G37834
15.6%
N23303
9.6%
O22290
9.2%
M18917
7.8%
A18917
7.8%
W3235
 
1.3%
H134
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter241967
100.0%

Most frequent character per category

ValueCountFrequency (%)
E39115
16.2%
R39111
16.2%
T39111
16.2%
G37834
15.6%
N23303
9.6%
O22290
9.2%
M18917
7.8%
A18917
7.8%
W3235
 
1.3%
H134
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin241967
100.0%

Most frequent character per script

ValueCountFrequency (%)
E39115
16.2%
R39111
16.2%
T39111
16.2%
G37834
15.6%
N23303
9.6%
O22290
9.2%
M18917
7.8%
A18917
7.8%
W3235
 
1.3%
H134
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII241967
100.0%

Most frequent character per block

ValueCountFrequency (%)
E39115
16.2%
R39111
16.2%
T39111
16.2%
G37834
15.6%
N23303
9.6%
O22290
9.2%
M18917
7.8%
A18917
7.8%
W3235
 
1.3%
H134
 
0.1%

annual_inc
Real number (ℝ≥0)

SKEWED

Distinct5577
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69204.0548
Minimum1896
Maximum6000000
Zeros0
Zeros (%)0.0%
Memory size331.0 KiB
2021-03-22T13:19:37.844840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1896
5-th percentile24000
Q140000
median59000
Q382500
95-th percentile144000
Maximum6000000
Range5998104
Interquartile range (IQR)42500

Descriptive statistics

Standard deviation64132.84555
Coefficient of variation (CV)0.9267209231
Kurtosis2121.405527
Mean69204.0548
Median Absolute Deviation (MAD)20000
Skewness29.09932812
Sum2930791721
Variance4113021878
MonotocityNot monotonic
2021-03-22T13:19:37.975836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600001589
 
3.8%
500001112
 
2.6%
40000932
 
2.2%
45000892
 
2.1%
30000879
 
2.1%
75000863
 
2.0%
65000840
 
2.0%
70000787
 
1.9%
48000763
 
1.8%
80000715
 
1.7%
Other values (5567)32978
77.9%
ValueCountFrequency (%)
18961
 
< 0.1%
20001
 
< 0.1%
33001
 
< 0.1%
35001
 
< 0.1%
36001
 
< 0.1%
40002
< 0.1%
40801
 
< 0.1%
42001
 
< 0.1%
45001
 
< 0.1%
48003
< 0.1%
ValueCountFrequency (%)
60000001
 
< 0.1%
39000001
 
< 0.1%
20397841
 
< 0.1%
19000001
 
< 0.1%
17820001
 
< 0.1%
14400002
< 0.1%
13620001
 
< 0.1%
12500001
 
< 0.1%
12000004
< 0.1%
11760001
 
< 0.1%

verification_status
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Not Verified
18640 
Verified
13434 
Source Verified
10276 

Length

Max length15
Median length12
Mean length11.4590791
Min length8

Characters and Unicode

Total characters485292
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVerified
2nd rowSource Verified
3rd rowNot Verified
4th rowSource Verified
5th rowSource Verified
ValueCountFrequency (%)
Not Verified18640
44.0%
Verified13434
31.7%
Source Verified10276
24.3%
2021-03-22T13:19:38.212828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-22T13:19:38.291825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
verified42350
59.4%
not18640
26.2%
source10276
 
14.4%

Most occurring characters

ValueCountFrequency (%)
e94976
19.6%
i84700
17.5%
r52626
10.8%
V42350
8.7%
f42350
8.7%
d42350
8.7%
o28916
 
6.0%
28916
 
6.0%
N18640
 
3.8%
t18640
 
3.8%
Other values (3)30828
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter385110
79.4%
Uppercase Letter71266
 
14.7%
Space Separator28916
 
6.0%

Most frequent character per category

ValueCountFrequency (%)
e94976
24.7%
i84700
22.0%
r52626
13.7%
f42350
11.0%
d42350
11.0%
o28916
 
7.5%
t18640
 
4.8%
u10276
 
2.7%
c10276
 
2.7%
ValueCountFrequency (%)
V42350
59.4%
N18640
26.2%
S10276
 
14.4%
ValueCountFrequency (%)
28916
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin456376
94.0%
Common28916
 
6.0%

Most frequent character per script

ValueCountFrequency (%)
e94976
20.8%
i84700
18.6%
r52626
11.5%
V42350
9.3%
f42350
9.3%
d42350
9.3%
o28916
 
6.3%
N18640
 
4.1%
t18640
 
4.1%
S10276
 
2.3%
Other values (2)20552
 
4.5%
ValueCountFrequency (%)
28916
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII485292
100.0%

Most frequent character per block

ValueCountFrequency (%)
e94976
19.6%
i84700
17.5%
r52626
10.8%
V42350
8.7%
f42350
8.7%
d42350
8.7%
o28916
 
6.0%
28916
 
6.0%
N18640
 
3.8%
t18640
 
3.8%
Other values (3)30828
 
6.4%

issue_d
Categorical

HIGH CARDINALITY

Distinct55
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Dec-2011
 
2266
Nov-2011
 
2226
Oct-2011
 
2113
Sep-2011
 
2061
Aug-2011
 
1933
Other values (50)
31751 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters338800
Distinct characters29
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDec-2011
2nd rowDec-2011
3rd rowDec-2011
4th rowDec-2011
5th rowDec-2011
ValueCountFrequency (%)
Dec-20112266
 
5.4%
Nov-20112226
 
5.3%
Oct-20112113
 
5.0%
Sep-20112061
 
4.9%
Aug-20111933
 
4.6%
Jul-20111866
 
4.4%
Jun-20111831
 
4.3%
May-20111700
 
4.0%
Apr-20111560
 
3.7%
Mar-20111441
 
3.4%
Other values (45)23353
55.1%
2021-03-22T13:19:38.527818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dec-20112266
 
5.4%
nov-20112226
 
5.3%
oct-20112113
 
5.0%
sep-20112061
 
4.9%
aug-20111933
 
4.6%
jul-20111866
 
4.4%
jun-20111831
 
4.3%
may-20111700
 
4.0%
apr-20111560
 
3.7%
mar-20111441
 
3.4%
Other values (45)23353
55.1%

Most occurring characters

ValueCountFrequency (%)
063025
18.6%
155829
16.5%
-42350
12.5%
242350
12.5%
e11120
 
3.3%
u10838
 
3.2%
J9728
 
2.9%
c8844
 
2.6%
a8747
 
2.6%
p6917
 
2.0%
Other values (19)79052
23.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number169400
50.0%
Lowercase Letter84700
25.0%
Uppercase Letter42350
 
12.5%
Dash Punctuation42350
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
e11120
13.1%
u10838
12.8%
c8844
10.4%
a8747
10.3%
p6917
8.2%
n6057
7.2%
r6041
7.1%
o4423
 
5.2%
v4423
 
5.2%
t4166
 
4.9%
Other values (4)13124
15.5%
ValueCountFrequency (%)
J9728
23.0%
A6771
16.0%
M6142
14.5%
D4678
11.0%
N4423
10.4%
O4166
9.8%
S3861
 
9.1%
F2581
 
6.1%
ValueCountFrequency (%)
063025
37.2%
155829
33.0%
242350
25.0%
95249
 
3.1%
82378
 
1.4%
7569
 
0.3%
ValueCountFrequency (%)
-42350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common211750
62.5%
Latin127050
37.5%

Most frequent character per script

ValueCountFrequency (%)
e11120
 
8.8%
u10838
 
8.5%
J9728
 
7.7%
c8844
 
7.0%
a8747
 
6.9%
p6917
 
5.4%
A6771
 
5.3%
M6142
 
4.8%
n6057
 
4.8%
r6041
 
4.8%
Other values (12)45845
36.1%
ValueCountFrequency (%)
063025
29.8%
155829
26.4%
-42350
20.0%
242350
20.0%
95249
 
2.5%
82378
 
1.1%
7569
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII338800
100.0%

Most frequent character per block

ValueCountFrequency (%)
063025
18.6%
155829
16.5%
-42350
12.5%
242350
12.5%
e11120
 
3.3%
u10838
 
3.2%
J9728
 
2.9%
c8844
 
2.6%
a8747
 
2.6%
p6917
 
2.0%
Other values (19)79052
23.3%

loan_status
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.8 MiB
Fully Paid
34072 
Charged Off
5584 
Does not meet the credit policy. Status:Fully Paid
 
1952
Does not meet the credit policy. Status:Charged Off
 
742

Length

Max length51
Median length10
Mean length12.6938843
Min length10

Characters and Unicode

Total characters537586
Distinct characters27
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFully Paid
2nd rowCharged Off
3rd rowFully Paid
4th rowFully Paid
5th rowFully Paid
ValueCountFrequency (%)
Fully Paid34072
80.5%
Charged Off5584
 
13.2%
Does not meet the credit policy. Status:Fully Paid1952
 
4.6%
Does not meet the credit policy. Status:Charged Off742
 
1.8%
2021-03-22T13:19:38.753811image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-22T13:19:38.827808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
paid36024
35.7%
fully34072
33.8%
off6326
 
6.3%
charged5584
 
5.5%
meet2694
 
2.7%
policy2694
 
2.7%
does2694
 
2.7%
the2694
 
2.7%
credit2694
 
2.7%
not2694
 
2.7%
Other values (2)2694
 
2.7%

Most occurring characters

ValueCountFrequency (%)
l74742
13.9%
58514
10.9%
a45044
8.4%
d45044
8.4%
i41412
 
7.7%
u38718
 
7.2%
y38718
 
7.2%
F36024
 
6.7%
P36024
 
6.7%
e19796
 
3.7%
Other values (17)103550
19.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter383596
71.4%
Uppercase Letter90088
 
16.8%
Space Separator58514
 
10.9%
Other Punctuation5388
 
1.0%

Most frequent character per category

ValueCountFrequency (%)
l74742
19.5%
a45044
11.7%
d45044
11.7%
i41412
10.8%
u38718
10.1%
y38718
10.1%
e19796
 
5.2%
t16164
 
4.2%
f12652
 
3.3%
h9020
 
2.4%
Other values (8)42286
11.0%
ValueCountFrequency (%)
F36024
40.0%
P36024
40.0%
C6326
 
7.0%
O6326
 
7.0%
D2694
 
3.0%
S2694
 
3.0%
ValueCountFrequency (%)
.2694
50.0%
:2694
50.0%
ValueCountFrequency (%)
58514
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin473684
88.1%
Common63902
 
11.9%

Most frequent character per script

ValueCountFrequency (%)
l74742
15.8%
a45044
9.5%
d45044
9.5%
i41412
8.7%
u38718
8.2%
y38718
8.2%
F36024
7.6%
P36024
7.6%
e19796
 
4.2%
t16164
 
3.4%
Other values (14)81998
17.3%
ValueCountFrequency (%)
58514
91.6%
.2694
 
4.2%
:2694
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII537586
100.0%

Most frequent character per block

ValueCountFrequency (%)
l74742
13.9%
58514
10.9%
a45044
8.4%
d45044
8.4%
i41412
 
7.7%
u38718
 
7.2%
y38718
 
7.2%
F36024
 
6.7%
P36024
 
6.7%
e19796
 
3.7%
Other values (17)103550
19.3%

desc
Categorical

HIGH CARDINALITY

Distinct28833
Distinct (%)68.1%
Missing0
Missing (%)0.0%
Memory size14.2 MiB
13463 
Debt Consolidation
 
11
Camping Membership
 
8
refinancing
 
5
consolidate debt
 
3
Other values (28828)
28860 

Length

Max length3988
Median length148
Mean length291.5066588
Min length1

Characters and Unicode

Total characters12345307
Distinct characters142
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28801 ?
Unique (%)68.0%

Sample

1st row Borrower added on 12/22/11 > I need to upgrade my business technologies.<br>
2nd row Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike. I only need this money because the deal im looking at is to good to pass up.<br><br> Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike.I only need this money because the deal im looking at is to good to pass up. I have finished college with an associates degree in business and its takingmeplaces<br>
3rd row
4th row Borrower added on 12/21/11 > to pay for property tax (borrow from friend, need to pay back) & central A/C need to be replace. I'm very sorry to let my loan expired last time.<br>
5th row Borrower added on 12/21/11 > I plan on combining three large interest bills together and freeing up some extra each month to pay toward other bills. I've always been a good payor but have found myself needing to make adjustments to my budget due to a medical scare. My job is very stable, I love it.<br>
ValueCountFrequency (%)
13463
31.8%
Debt Consolidation11
 
< 0.1%
Camping Membership8
 
< 0.1%
refinancing5
 
< 0.1%
consolidate debt3
 
< 0.1%
credit card consolidation3
 
< 0.1%
Personal Loan3
 
< 0.1%
credit card debt consolidation3
 
< 0.1%
personal loan3
 
< 0.1%
debt consolidation3
 
< 0.1%
Other values (28823)28845
68.1%
2021-03-22T13:19:39.181797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
i84693
 
3.8%
to77346
 
3.5%
a59762
 
2.7%
and59065
 
2.7%
the58882
 
2.7%
my55619
 
2.5%
on51807
 
2.3%
39040
 
1.8%
for35338
 
1.6%
have35037
 
1.6%
Other values (56623)1661550
74.9%

Most occurring characters

ValueCountFrequency (%)
2307721
18.7%
e1031955
 
8.4%
a772858
 
6.3%
o764579
 
6.2%
t704519
 
5.7%
n661771
 
5.4%
r631880
 
5.1%
i537842
 
4.4%
s462167
 
3.7%
d427352
 
3.5%
Other values (132)4042663
32.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8796758
71.3%
Space Separator2307795
 
18.7%
Decimal Number367380
 
3.0%
Other Punctuation349350
 
2.8%
Uppercase Letter327662
 
2.7%
Math Symbol146103
 
1.2%
Currency Symbol18129
 
0.1%
Dash Punctuation14245
 
0.1%
Close Punctuation7928
 
0.1%
Open Punctuation7263
 
0.1%
Other values (7)2694
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
I102971
31.4%
B36359
 
11.1%
T30751
 
9.4%
A16938
 
5.2%
M15426
 
4.7%
C15373
 
4.7%
S10593
 
3.2%
E10059
 
3.1%
W9654
 
2.9%
L9352
 
2.9%
Other values (21)70186
21.4%
ValueCountFrequency (%)
e1031955
11.7%
a772858
 
8.8%
o764579
 
8.7%
t704519
 
8.0%
n661771
 
7.5%
r631880
 
7.2%
i537842
 
6.1%
s462167
 
5.3%
d427352
 
4.9%
l385328
 
4.4%
Other values (18)2416507
27.5%
ValueCountFrequency (%)
.130861
37.5%
/121300
34.7%
,54510
15.6%
'14440
 
4.1%
!7302
 
2.1%
%6183
 
1.8%
:5779
 
1.7%
;3486
 
1.0%
&2746
 
0.8%
"913
 
0.3%
Other values (10)1830
 
0.5%
ValueCountFrequency (%)
1307
60.3%
€422
 
19.5%
™200
 
9.2%
“38
 
1.8%
’37
 
1.7%
‚30
 
1.4%
29
 
1.3%
œ25
 
1.2%
ƒ24
 
1.1%
š15
 
0.7%
Other values (9)39
 
1.8%
ValueCountFrequency (%)
0111519
30.4%
1101332
27.6%
239001
 
10.6%
523119
 
6.3%
319032
 
5.2%
917399
 
4.7%
414540
 
4.0%
614047
 
3.8%
713734
 
3.7%
813657
 
3.7%
ValueCountFrequency (%)
>88128
60.3%
<55923
38.3%
+1051
 
0.7%
=646
 
0.4%
~317
 
0.2%
¬28
 
< 0.1%
|10
 
< 0.1%
ValueCountFrequency (%)
¦93
80.2%
©15
 
12.9%
6
 
5.2%
®2
 
1.7%
ValueCountFrequency (%)
(7219
99.4%
[41
 
0.6%
{3
 
< 0.1%
ValueCountFrequency (%)
)7883
99.4%
]41
 
0.5%
}4
 
0.1%
ValueCountFrequency (%)
-14230
99.9%
10
 
0.1%
5
 
< 0.1%
ValueCountFrequency (%)
`13
68.4%
^5
 
26.3%
¯1
 
5.3%
ValueCountFrequency (%)
2307721
> 99.9%
 74
 
< 0.1%
ValueCountFrequency (%)
$18061
99.6%
¢68
 
0.4%
ValueCountFrequency (%)
½7
87.5%
¾1
 
12.5%
ValueCountFrequency (%)
89
84.8%
16
 
15.2%
ValueCountFrequency (%)
16
84.2%
3
 
15.8%
ValueCountFrequency (%)
_261
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9124420
73.9%
Common3220887
 
26.1%

Most frequent character per script

ValueCountFrequency (%)
2307721
71.6%
.130861
 
4.1%
/121300
 
3.8%
0111519
 
3.5%
1101332
 
3.1%
>88128
 
2.7%
<55923
 
1.7%
,54510
 
1.7%
239001
 
1.2%
523119
 
0.7%
Other values (73)187473
 
5.8%
ValueCountFrequency (%)
e1031955
 
11.3%
a772858
 
8.5%
o764579
 
8.4%
t704519
 
7.7%
n661771
 
7.3%
r631880
 
6.9%
i537842
 
5.9%
s462167
 
5.1%
d427352
 
4.7%
l385328
 
4.2%
Other values (49)2744169
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII12343296
> 99.9%
None1836
 
< 0.1%
Punctuation169
 
< 0.1%
Specials6
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
2307721
18.7%
e1031955
 
8.4%
a772858
 
6.3%
o764579
 
6.2%
t704519
 
5.7%
n661771
 
5.4%
r631880
 
5.1%
i537842
 
4.4%
s462167
 
3.7%
d427352
 
3.5%
Other values (86)4040652
32.7%
ValueCountFrequency (%)
â447
24.3%
€422
23.0%
™200
10.9%
Â123
 
6.7%
¦93
 
5.1%
Ã89
 
4.8%
 74
 
4.0%
¢68
 
3.7%
“38
 
2.1%
’37
 
2.0%
Other values (27)245
13.3%
ValueCountFrequency (%)
89
52.7%
22
 
13.0%
16
 
9.5%
16
 
9.5%
10
 
5.9%
8
 
4.7%
5
 
3.0%
3
 
1.8%
ValueCountFrequency (%)
6
100.0%

purpose
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.9 MiB
debt_consolidation
19724 
credit_card
5469 
other
4365 
home_improvement
3189 
major_purchase
2298 
Other values (9)
7305 

Length

Max length18
Median length16
Mean length13.70229044
Min length3

Characters and Unicode

Total characters580292
Distinct characters22
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowcar
3rd rowsmall_business
4th rowother
5th rowother
ValueCountFrequency (%)
debt_consolidation19724
46.6%
credit_card5469
 
12.9%
other4365
 
10.3%
home_improvement3189
 
7.5%
major_purchase2298
 
5.4%
small_business1980
 
4.7%
car1611
 
3.8%
wedding997
 
2.4%
medical751
 
1.8%
moving625
 
1.5%
Other values (4)1341
 
3.2%
2021-03-22T13:19:39.475787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debt_consolidation19724
46.6%
credit_card5469
 
12.9%
other4365
 
10.3%
home_improvement3189
 
7.5%
major_purchase2298
 
5.4%
small_business1980
 
4.7%
car1611
 
3.8%
wedding997
 
2.4%
medical751
 
1.8%
moving625
 
1.5%
Other values (4)1341
 
3.2%

Most occurring characters

ValueCountFrequency (%)
o74074
12.8%
d53545
9.2%
t53284
9.2%
i53272
9.2%
n47262
8.1%
e46513
 
8.0%
c36135
 
6.2%
a35862
 
6.2%
_32765
 
5.6%
s30365
 
5.2%
Other values (12)117215
20.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter547527
94.4%
Connector Punctuation32765
 
5.6%

Most frequent character per category

ValueCountFrequency (%)
o74074
13.5%
d53545
9.8%
t53284
9.7%
i53272
9.7%
n47262
8.6%
e46513
8.5%
c36135
 
6.6%
a35862
 
6.5%
s30365
 
5.5%
l24954
 
4.6%
Other values (11)92261
16.9%
ValueCountFrequency (%)
_32765
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin547527
94.4%
Common32765
 
5.6%

Most frequent character per script

ValueCountFrequency (%)
o74074
13.5%
d53545
9.8%
t53284
9.7%
i53272
9.7%
n47262
8.6%
e46513
8.5%
c36135
 
6.6%
a35862
 
6.5%
s30365
 
5.5%
l24954
 
4.6%
Other values (11)92261
16.9%
ValueCountFrequency (%)
_32765
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII580292
100.0%

Most frequent character per block

ValueCountFrequency (%)
o74074
12.8%
d53545
9.2%
t53284
9.2%
i53272
9.2%
n47262
8.1%
e46513
 
8.0%
c36135
 
6.2%
a35862
 
6.2%
_32765
 
5.6%
s30365
 
5.2%
Other values (12)117215
20.2%

title
Categorical

HIGH CARDINALITY

Distinct21173
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size3.0 MiB
Debt Consolidation
 
2255
Debt Consolidation Loan
 
1755
Personal Loan
 
706
Consolidation
 
545
debt consolidation
 
532
Other values (21168)
36557 

Length

Max length80
Median length16
Mean length17.34420307
Min length2

Characters and Unicode

Total characters734527
Distinct characters108
Distinct categories15 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19031 ?
Unique (%)44.9%

Sample

1st rowComputer
2nd rowbike
3rd rowreal estate business
4th rowpersonel
5th rowPersonal
ValueCountFrequency (%)
Debt Consolidation2255
 
5.3%
Debt Consolidation Loan1755
 
4.1%
Personal Loan706
 
1.7%
Consolidation545
 
1.3%
debt consolidation532
 
1.3%
Home Improvement372
 
0.9%
Credit Card Consolidation370
 
0.9%
Debt consolidation346
 
0.8%
Small Business Loan332
 
0.8%
Personal330
 
0.8%
Other values (21163)34807
82.2%
2021-03-22T13:19:39.803777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
loan11460
 
10.2%
debt9705
 
8.6%
consolidation8976
 
8.0%
credit4945
 
4.4%
card3552
 
3.2%
personal2210
 
2.0%
home2017
 
1.8%
pay1468
 
1.3%
off1376
 
1.2%
to1275
 
1.1%
Other values (9359)65766
58.3%

Most occurring characters

ValueCountFrequency (%)
71669
 
9.8%
o69979
 
9.5%
n59526
 
8.1%
e59102
 
8.0%
a53721
 
7.3%
i47064
 
6.4%
t45804
 
6.2%
d32909
 
4.5%
r31649
 
4.3%
s30868
 
4.2%
Other values (98)232236
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter561157
76.4%
Uppercase Letter88905
 
12.1%
Space Separator71669
 
9.8%
Decimal Number6385
 
0.9%
Other Punctuation4894
 
0.7%
Dash Punctuation867
 
0.1%
Connector Punctuation220
 
< 0.1%
Close Punctuation111
 
< 0.1%
Currency Symbol104
 
< 0.1%
Math Symbol103
 
< 0.1%
Other values (5)112
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
C19602
22.0%
L10859
12.2%
D9679
10.9%
P6046
 
6.8%
R4034
 
4.5%
S3513
 
4.0%
M3484
 
3.9%
B3325
 
3.7%
H3173
 
3.6%
E3130
 
3.5%
Other values (18)22060
24.8%
ValueCountFrequency (%)
o69979
12.5%
n59526
10.6%
e59102
10.5%
a53721
9.6%
i47064
8.4%
t45804
8.2%
d32909
 
5.9%
r31649
 
5.6%
s30868
 
5.5%
l28219
 
5.0%
Other values (18)102316
18.2%
ValueCountFrequency (%)
!1249
25.5%
'1066
21.8%
.810
16.6%
/587
12.0%
,480
 
9.8%
&352
 
7.2%
%105
 
2.1%
:65
 
1.3%
"61
 
1.2%
?27
 
0.6%
Other values (5)92
 
1.9%
ValueCountFrequency (%)
01819
28.5%
11756
27.5%
21186
18.6%
3315
 
4.9%
5275
 
4.3%
9263
 
4.1%
4230
 
3.6%
6195
 
3.1%
8175
 
2.7%
7171
 
2.7%
ValueCountFrequency (%)
€4
21.1%
4
21.1%
—4
21.1%
2
10.5%
™2
10.5%
–1
 
5.3%
‚1
 
5.3%
…1
 
5.3%
ValueCountFrequency (%)
+63
61.2%
=19
 
18.4%
<9
 
8.7%
>8
 
7.8%
~3
 
2.9%
|1
 
1.0%
ValueCountFrequency (%)
^1
33.3%
´1
33.3%
`1
33.3%
ValueCountFrequency (%)
(84
96.6%
[3
 
3.4%
ValueCountFrequency (%)
)107
96.4%
]4
 
3.6%
ValueCountFrequency (%)
71669
100.0%
ValueCountFrequency (%)
-867
100.0%
ValueCountFrequency (%)
_220
100.0%
ValueCountFrequency (%)
$104
100.0%
ValueCountFrequency (%)
³1
100.0%
ValueCountFrequency (%)
¦2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin650062
88.5%
Common84465
 
11.5%

Most frequent character per script

ValueCountFrequency (%)
o69979
 
10.8%
n59526
 
9.2%
e59102
 
9.1%
a53721
 
8.3%
i47064
 
7.2%
t45804
 
7.0%
d32909
 
5.1%
r31649
 
4.9%
s30868
 
4.7%
l28219
 
4.3%
Other values (46)191221
29.4%
ValueCountFrequency (%)
71669
84.9%
01819
 
2.2%
11756
 
2.1%
!1249
 
1.5%
21186
 
1.4%
'1066
 
1.3%
-867
 
1.0%
.810
 
1.0%
/587
 
0.7%
,480
 
0.6%
Other values (42)2976
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII734495
> 99.9%
None32
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
71669
 
9.8%
o69979
 
9.5%
n59526
 
8.1%
e59102
 
8.0%
a53721
 
7.3%
i47064
 
6.4%
t45804
 
6.2%
d32909
 
4.5%
r31649
 
4.3%
s30868
 
4.2%
Other values (84)232204
31.6%
ValueCountFrequency (%)
â4
12.5%
€4
12.5%
î4
12.5%
4
12.5%
—4
12.5%
Ã2
6.2%
™2
6.2%
¦2
6.2%
–1
 
3.1%
‚1
 
3.1%
Other values (4)4
12.5%

zip_code
Categorical

HIGH CARDINALITY

Distinct836
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
100xx
 
639
945xx
 
558
606xx
 
546
112xx
 
536
070xx
 
499
Other values (831)
39572 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters211750
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)0.1%

Sample

1st row860xx
2nd row309xx
3rd row606xx
4th row917xx
5th row972xx
ValueCountFrequency (%)
100xx639
 
1.5%
945xx558
 
1.3%
606xx546
 
1.3%
112xx536
 
1.3%
070xx499
 
1.2%
900xx474
 
1.1%
300xx436
 
1.0%
021xx414
 
1.0%
750xx391
 
0.9%
926xx385
 
0.9%
Other values (826)37472
88.5%
2021-03-22T13:19:40.097767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100xx639
 
1.5%
945xx558
 
1.3%
606xx546
 
1.3%
112xx536
 
1.3%
070xx499
 
1.2%
900xx474
 
1.1%
300xx436
 
1.0%
021xx414
 
1.0%
750xx391
 
0.9%
926xx385
 
0.9%
Other values (826)37472
88.5%

Most occurring characters

ValueCountFrequency (%)
x84700
40.0%
021162
 
10.0%
116600
 
7.8%
214373
 
6.8%
913366
 
6.3%
313295
 
6.3%
710928
 
5.2%
49754
 
4.6%
59617
 
4.5%
89274
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number127050
60.0%
Lowercase Letter84700
40.0%

Most frequent character per category

ValueCountFrequency (%)
021162
16.7%
116600
13.1%
214373
11.3%
913366
10.5%
313295
10.5%
710928
8.6%
49754
7.7%
59617
7.6%
89274
7.3%
68681
6.8%
ValueCountFrequency (%)
x84700
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common127050
60.0%
Latin84700
40.0%

Most frequent character per script

ValueCountFrequency (%)
021162
16.7%
116600
13.1%
214373
11.3%
913366
10.5%
313295
10.5%
710928
8.6%
49754
7.7%
59617
7.6%
89274
7.3%
68681
6.8%
ValueCountFrequency (%)
x84700
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII211750
100.0%

Most frequent character per block

ValueCountFrequency (%)
x84700
40.0%
021162
 
10.0%
116600
 
7.8%
214373
 
6.8%
913366
 
6.3%
313295
 
6.3%
710928
 
5.2%
49754
 
4.6%
59617
 
4.5%
89274
 
4.4%

addr_state
Categorical

Distinct50
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
CA
7398 
NY
4045 
FL
3093 
TX
2896 
NJ
 
1976
Other values (45)
22942 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters84700
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAZ
2nd rowGA
3rd rowIL
4th rowCA
5th rowOR
ValueCountFrequency (%)
CA7398
17.5%
NY4045
 
9.6%
FL3093
 
7.3%
TX2896
 
6.8%
NJ1976
 
4.7%
IL1667
 
3.9%
PA1643
 
3.9%
GA1500
 
3.5%
VA1481
 
3.5%
MA1432
 
3.4%
Other values (40)15219
35.9%
2021-03-22T13:19:40.361759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca7398
17.5%
ny4045
 
9.6%
fl3093
 
7.3%
tx2896
 
6.8%
nj1976
 
4.7%
il1667
 
3.9%
pa1643
 
3.9%
ga1500
 
3.5%
va1481
 
3.5%
ma1432
 
3.4%
Other values (40)15219
35.9%

Most occurring characters

ValueCountFrequency (%)
A16568
19.6%
C10602
12.5%
N8477
10.0%
L5702
 
6.7%
M5081
 
6.0%
Y4488
 
5.3%
T4171
 
4.9%
O3717
 
4.4%
I3403
 
4.0%
F3093
 
3.7%
Other values (14)19398
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter84700
100.0%

Most frequent character per category

ValueCountFrequency (%)
A16568
19.6%
C10602
12.5%
N8477
10.0%
L5702
 
6.7%
M5081
 
6.0%
Y4488
 
5.3%
T4171
 
4.9%
O3717
 
4.4%
I3403
 
4.0%
F3093
 
3.7%
Other values (14)19398
22.9%

Most occurring scripts

ValueCountFrequency (%)
Latin84700
100.0%

Most frequent character per script

ValueCountFrequency (%)
A16568
19.6%
C10602
12.5%
N8477
10.0%
L5702
 
6.7%
M5081
 
6.0%
Y4488
 
5.3%
T4171
 
4.9%
O3717
 
4.4%
I3403
 
4.0%
F3093
 
3.7%
Other values (14)19398
22.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII84700
100.0%

Most frequent character per block

ValueCountFrequency (%)
A16568
19.6%
C10602
12.5%
N8477
10.0%
L5702
 
6.7%
M5081
 
6.0%
Y4488
 
5.3%
T4171
 
4.9%
O3717
 
4.4%
I3403
 
4.0%
F3093
 
3.7%
Other values (14)19398
22.9%

dti
Real number (ℝ≥0)

Distinct2894
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.38360496
Minimum0
Maximum29.99
Zeros199
Zeros (%)0.5%
Memory size331.0 KiB
2021-03-22T13:19:40.478764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.11
Q18.21
median13.48
Q318.69
95-th percentile23.92
Maximum29.99
Range29.99
Interquartile range (IQR)10.48

Descriptive statistics

Standard deviation6.72334128
Coefficient of variation (CV)0.5023565251
Kurtosis-0.8509484411
Mean13.38360496
Median Absolute Deviation (MAD)5.24
Skewness-0.03053296036
Sum566795.67
Variance45.20331797
MonotocityNot monotonic
2021-03-22T13:19:40.607760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0199
 
0.5%
1254
 
0.1%
1846
 
0.1%
19.245
 
0.1%
13.243
 
0.1%
13.541
 
0.1%
16.841
 
0.1%
12.4840
 
0.1%
14.2938
 
0.1%
4.837
 
0.1%
Other values (2884)41766
98.6%
ValueCountFrequency (%)
0199
0.5%
0.013
 
< 0.1%
0.025
 
< 0.1%
0.032
 
< 0.1%
0.043
 
< 0.1%
0.052
 
< 0.1%
0.061
 
< 0.1%
0.075
 
< 0.1%
0.085
 
< 0.1%
0.094
 
< 0.1%
ValueCountFrequency (%)
29.991
 
< 0.1%
29.961
 
< 0.1%
29.952
< 0.1%
29.933
< 0.1%
29.922
< 0.1%
29.91
 
< 0.1%
29.891
 
< 0.1%
29.881
 
< 0.1%
29.862
< 0.1%
29.851
 
< 0.1%

delinq_2yrs
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.152325856
Minimum0
Maximum13
Zeros37632
Zeros (%)88.9%
Memory size331.0 KiB
2021-03-22T13:19:40.721756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum13
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.5118802971
Coefficient of variation (CV)3.360429481
Kurtosis51.29476705
Mean0.152325856
Median Absolute Deviation (MAD)0
Skewness5.439434812
Sum6451
Variance0.2620214386
MonotocityNot monotonic
2021-03-22T13:19:40.829752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
037632
88.9%
13584
 
8.5%
2769
 
1.8%
3242
 
0.6%
470
 
0.2%
527
 
0.1%
613
 
< 0.1%
76
 
< 0.1%
83
 
< 0.1%
112
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
037632
88.9%
13584
 
8.5%
2769
 
1.8%
3242
 
0.6%
470
 
0.2%
527
 
0.1%
613
 
< 0.1%
76
 
< 0.1%
83
 
< 0.1%
91
 
< 0.1%
ValueCountFrequency (%)
131
 
< 0.1%
112
 
< 0.1%
91
 
< 0.1%
83
 
< 0.1%
76
 
< 0.1%
613
 
< 0.1%
527
 
0.1%
470
 
0.2%
3242
 
0.6%
2769
1.8%

earliest_cr_line
Categorical

HIGH CARDINALITY

Distinct530
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Oct-1999
 
393
Nov-1998
 
390
Oct-2000
 
368
Dec-1998
 
366
Dec-1997
 
347
Other values (525)
40486 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters338800
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)0.1%

Sample

1st rowJan-1985
2nd rowApr-1999
3rd rowNov-2001
4th rowFeb-1996
5th rowJan-1996
ValueCountFrequency (%)
Oct-1999393
 
0.9%
Nov-1998390
 
0.9%
Oct-2000368
 
0.9%
Dec-1998366
 
0.9%
Dec-1997347
 
0.8%
Nov-2000339
 
0.8%
Nov-1999335
 
0.8%
Oct-1998332
 
0.8%
Sep-2000325
 
0.8%
Nov-1997318
 
0.8%
Other values (520)38837
91.7%
2021-03-22T13:19:41.114744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
oct-1999393
 
0.9%
nov-1998390
 
0.9%
oct-2000368
 
0.9%
dec-1998366
 
0.9%
dec-1997347
 
0.8%
nov-2000339
 
0.8%
nov-1999335
 
0.8%
oct-1998332
 
0.8%
sep-2000325
 
0.8%
nov-1997318
 
0.8%
Other values (520)38837
91.7%

Most occurring characters

ValueCountFrequency (%)
951392
15.2%
-42350
 
12.5%
036460
 
10.8%
130461
 
9.0%
219346
 
5.7%
e11188
 
3.3%
J10067
 
3.0%
u9917
 
2.9%
a9738
 
2.9%
89006
 
2.7%
Other values (23)108875
32.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number169400
50.0%
Lowercase Letter84700
25.0%
Uppercase Letter42350
 
12.5%
Dash Punctuation42350
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
e11188
13.2%
u9917
11.7%
a9738
11.5%
c8674
10.2%
n6802
8.0%
p6770
8.0%
r5933
7.0%
t4371
 
5.2%
o4182
 
4.9%
v4182
 
4.9%
Other values (4)12943
15.3%
ValueCountFrequency (%)
951392
30.3%
036460
21.5%
130461
18.0%
219346
 
11.4%
89006
 
5.3%
75146
 
3.0%
44549
 
2.7%
64532
 
2.7%
54495
 
2.7%
34013
 
2.4%
ValueCountFrequency (%)
J10067
23.8%
A6467
15.3%
M6075
14.3%
O4371
10.3%
D4303
10.2%
N4182
9.9%
S3816
 
9.0%
F3069
 
7.2%
ValueCountFrequency (%)
-42350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common211750
62.5%
Latin127050
37.5%

Most frequent character per script

ValueCountFrequency (%)
e11188
 
8.8%
J10067
 
7.9%
u9917
 
7.8%
a9738
 
7.7%
c8674
 
6.8%
n6802
 
5.4%
p6770
 
5.3%
A6467
 
5.1%
M6075
 
4.8%
r5933
 
4.7%
Other values (12)45419
35.7%
ValueCountFrequency (%)
951392
24.3%
-42350
20.0%
036460
17.2%
130461
14.4%
219346
 
9.1%
89006
 
4.3%
75146
 
2.4%
44549
 
2.1%
64532
 
2.1%
54495
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII338800
100.0%

Most frequent character per block

ValueCountFrequency (%)
951392
15.2%
-42350
 
12.5%
036460
 
10.8%
130461
 
9.0%
219346
 
5.7%
e11188
 
3.3%
J10067
 
3.0%
u9917
 
2.9%
a9738
 
2.9%
89006
 
2.7%
Other values (23)108875
32.1%

fico_range_low
Real number (ℝ≥0)

HIGH CORRELATION

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean713.0929162
Minimum610
Maximum825
Zeros0
Zeros (%)0.0%
Memory size331.0 KiB
2021-03-22T13:19:41.237732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum610
5-th percentile665
Q1685
median710
Q3740
95-th percentile780
Maximum825
Range215
Interquartile range (IQR)55

Descriptive statistics

Standard deviation36.18989634
Coefficient of variation (CV)0.05075060419
Kurtosis-0.4980942286
Mean713.0929162
Median Absolute Deviation (MAD)25
Skewness0.4634056282
Sum30199485
Variance1309.708597
MonotocityNot monotonic
2021-03-22T13:19:41.359728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
6852301
 
5.4%
7002258
 
5.3%
6802213
 
5.2%
6952187
 
5.2%
6902182
 
5.2%
6751985
 
4.7%
7051964
 
4.6%
7201939
 
4.6%
7251889
 
4.5%
7151882
 
4.4%
Other values (34)21550
50.9%
ValueCountFrequency (%)
6102
 
< 0.1%
6151
 
< 0.1%
6201
 
< 0.1%
6252
 
< 0.1%
6306
 
< 0.1%
6355
 
< 0.1%
640100
0.2%
645112
0.3%
650131
0.3%
655127
0.3%
ValueCountFrequency (%)
8253
 
< 0.1%
82019
 
< 0.1%
81528
 
0.1%
810125
 
0.3%
805193
 
0.5%
800253
0.6%
795336
0.8%
790421
1.0%
785405
1.0%
780572
1.4%

fico_range_high
Real number (ℝ≥0)

HIGH CORRELATION

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean717.0929162
Minimum614
Maximum829
Zeros0
Zeros (%)0.0%
Memory size331.0 KiB
2021-03-22T13:19:41.484724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum614
5-th percentile669
Q1689
median714
Q3744
95-th percentile784
Maximum829
Range215
Interquartile range (IQR)55

Descriptive statistics

Standard deviation36.18989634
Coefficient of variation (CV)0.05046751338
Kurtosis-0.4980942286
Mean717.0929162
Median Absolute Deviation (MAD)25
Skewness0.4634056282
Sum30368885
Variance1309.708597
MonotocityNot monotonic
2021-03-22T13:19:41.599720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
6892301
 
5.4%
7042258
 
5.3%
6842213
 
5.2%
6992187
 
5.2%
6942182
 
5.2%
6791985
 
4.7%
7091964
 
4.6%
7241939
 
4.6%
7291889
 
4.5%
7191882
 
4.4%
Other values (34)21550
50.9%
ValueCountFrequency (%)
6142
 
< 0.1%
6191
 
< 0.1%
6241
 
< 0.1%
6292
 
< 0.1%
6346
 
< 0.1%
6395
 
< 0.1%
644100
0.2%
649112
0.3%
654131
0.3%
659127
0.3%
ValueCountFrequency (%)
8293
 
< 0.1%
82419
 
< 0.1%
81928
 
0.1%
814125
 
0.3%
809193
 
0.5%
804253
0.6%
799336
0.8%
794421
1.0%
789405
1.0%
784572
1.4%

inq_last_6mths
Real number (ℝ≥0)

ZEROS

Distinct28
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.079645809
Minimum0
Maximum33
Zeros19606
Zeros (%)46.3%
Memory size331.0 KiB
2021-03-22T13:19:41.715716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum33
Range33
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.526551198
Coefficient of variation (CV)1.413937039
Kurtosis31.14815404
Mean1.079645809
Median Absolute Deviation (MAD)1
Skewness3.464906217
Sum45723
Variance2.330358561
MonotocityNot monotonic
2021-03-22T13:19:41.826713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
019606
46.3%
111204
26.5%
25962
 
14.1%
33164
 
7.5%
41048
 
2.5%
5592
 
1.4%
6334
 
0.8%
7181
 
0.4%
8114
 
0.3%
950
 
0.1%
Other values (18)95
 
0.2%
ValueCountFrequency (%)
019606
46.3%
111204
26.5%
25962
 
14.1%
33164
 
7.5%
41048
 
2.5%
5592
 
1.4%
6334
 
0.8%
7181
 
0.4%
8114
 
0.3%
950
 
0.1%
ValueCountFrequency (%)
331
 
< 0.1%
321
 
< 0.1%
311
 
< 0.1%
281
 
< 0.1%
271
 
< 0.1%
251
 
< 0.1%
242
< 0.1%
201
 
< 0.1%
192
< 0.1%
184
< 0.1%

open_acc
Real number (ℝ≥0)

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.355088548
Minimum1
Maximum47
Zeros0
Zeros (%)0.0%
Memory size331.0 KiB
2021-03-22T13:19:41.951709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median9
Q312
95-th percentile18
Maximum47
Range46
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.493806239
Coefficient of variation (CV)0.4803595622
Kurtosis1.941439942
Mean9.355088548
Median Absolute Deviation (MAD)3
Skewness1.044050346
Sum396188
Variance20.19429452
MonotocityNot monotonic
2021-03-22T13:19:42.087709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
74237
10.0%
84163
9.8%
64156
9.8%
93911
9.2%
103383
 
8.0%
53358
 
7.9%
112939
 
6.9%
42489
 
5.9%
122395
 
5.7%
132054
 
4.9%
Other values (34)9265
21.9%
ValueCountFrequency (%)
134
 
0.1%
2667
 
1.6%
31592
 
3.8%
42489
5.9%
53358
7.9%
64156
9.8%
74237
10.0%
84163
9.8%
93911
9.2%
103383
8.0%
ValueCountFrequency (%)
471
 
< 0.1%
461
 
< 0.1%
441
 
< 0.1%
421
 
< 0.1%
411
 
< 0.1%
391
 
< 0.1%
382
< 0.1%
371
 
< 0.1%
362
< 0.1%
354
< 0.1%

pub_rec
Real number (ℝ≥0)

ZEROS

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05813459268
Minimum0
Maximum5
Zeros39984
Zeros (%)94.4%
Memory size331.0 KiB
2021-03-22T13:19:42.197701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.2457157345
Coefficient of variation (CV)4.226669925
Kurtosis26.89432834
Mean0.05813459268
Median Absolute Deviation (MAD)0
Skewness4.609349912
Sum2462
Variance0.06037622217
MonotocityNot monotonic
2021-03-22T13:19:43.171670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
039984
94.4%
12288
 
5.4%
264
 
0.2%
311
 
< 0.1%
42
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
039984
94.4%
12288
 
5.4%
264
 
0.2%
311
 
< 0.1%
42
 
< 0.1%
51
 
< 0.1%
ValueCountFrequency (%)
51
 
< 0.1%
42
 
< 0.1%
311
 
< 0.1%
264
 
0.2%
12288
 
5.4%
039984
94.4%

revol_bal
Real number (ℝ≥0)

ZEROS

Distinct22691
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14330.05962
Minimum0
Maximum1207359
Zeros1028
Zeros (%)2.4%
Memory size331.0 KiB
2021-03-22T13:19:43.301665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile319
Q13670
median8858
Q317283.75
95-th percentile44587.1
Maximum1207359
Range1207359
Interquartile range (IQR)13613.75

Descriptive statistics

Standard deviation21986.93728
Coefficient of variation (CV)1.534322805
Kurtosis349.0122448
Mean14330.05962
Median Absolute Deviation (MAD)6097
Skewness11.03939013
Sum606878025
Variance483425411.1
MonotocityNot monotonic
2021-03-22T13:19:43.454659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01028
 
2.4%
25514
 
< 0.1%
29814
 
< 0.1%
113
 
< 0.1%
68212
 
< 0.1%
610
 
< 0.1%
40010
 
< 0.1%
5210
 
< 0.1%
3910
 
< 0.1%
52729
 
< 0.1%
Other values (22681)41220
97.3%
ValueCountFrequency (%)
01028
2.4%
113
 
< 0.1%
26
 
< 0.1%
37
 
< 0.1%
43
 
< 0.1%
58
 
< 0.1%
610
 
< 0.1%
76
 
< 0.1%
85
 
< 0.1%
98
 
< 0.1%
ValueCountFrequency (%)
12073591
< 0.1%
9520131
< 0.1%
6025191
< 0.1%
5089611
< 0.1%
4875891
< 0.1%
4657311
< 0.1%
4231891
< 0.1%
4077941
< 0.1%
4019411
< 0.1%
3941071
< 0.1%

revol_util
Categorical

HIGH CARDINALITY

Distinct1119
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
0%
 
1068
40.7%
 
65
0.2%
 
63
63%
 
63
66.6%
 
62
Other values (1114)
41029 

Length

Max length6
Median length5
Mean length4.646328217
Min length2

Characters and Unicode

Total characters196772
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique115 ?
Unique (%)0.3%

Sample

1st row83.7%
2nd row9.4%
3rd row98.5%
4th row21%
5th row53.9%
ValueCountFrequency (%)
0%1068
 
2.5%
40.7%65
 
0.2%
0.2%63
 
0.1%
63%63
 
0.1%
66.6%62
 
0.1%
0.1%61
 
0.1%
70.4%61
 
0.1%
64.6%60
 
0.1%
37.6%60
 
0.1%
46.4%59
 
0.1%
Other values (1109)40728
96.2%
2021-03-22T13:19:43.762650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01068
 
2.5%
40.765
 
0.2%
6363
 
0.1%
0.263
 
0.1%
66.662
 
0.1%
70.461
 
0.1%
0.161
 
0.1%
37.660
 
0.1%
64.660
 
0.1%
66.759
 
0.1%
Other values (1109)40728
96.2%

Most occurring characters

ValueCountFrequency (%)
%42350
21.5%
.37165
18.9%
412893
 
6.6%
512878
 
6.5%
612815
 
6.5%
712812
 
6.5%
312631
 
6.4%
212303
 
6.3%
812234
 
6.2%
111797
 
6.0%
Other values (2)16894
 
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number117257
59.6%
Other Punctuation79515
40.4%

Most frequent character per category

ValueCountFrequency (%)
412893
11.0%
512878
11.0%
612815
10.9%
712812
10.9%
312631
10.8%
212303
10.5%
812234
10.4%
111797
10.1%
911580
9.9%
05314
4.5%
ValueCountFrequency (%)
%42350
53.3%
.37165
46.7%

Most occurring scripts

ValueCountFrequency (%)
Common196772
100.0%

Most frequent character per script

ValueCountFrequency (%)
%42350
21.5%
.37165
18.9%
412893
 
6.6%
512878
 
6.5%
612815
 
6.5%
712812
 
6.5%
312631
 
6.4%
212303
 
6.3%
812234
 
6.2%
111797
 
6.0%
Other values (2)16894
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII196772
100.0%

Most frequent character per block

ValueCountFrequency (%)
%42350
21.5%
.37165
18.9%
412893
 
6.6%
512878
 
6.5%
612815
 
6.5%
712812
 
6.5%
312631
 
6.4%
212303
 
6.3%
812234
 
6.2%
111797
 
6.0%
Other values (2)16894
 
8.6%

total_acc
Real number (ℝ≥0)

Distinct83
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.15279811
Minimum1
Maximum90
Zeros0
Zeros (%)0.0%
Memory size331.0 KiB
2021-03-22T13:19:43.899646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q113
median20
Q329
95-th percentile44
Maximum90
Range89
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.585441
Coefficient of variation (CV)0.5229786747
Kurtosis0.6627622935
Mean22.15279811
Median Absolute Deviation (MAD)8
Skewness0.8230335865
Sum938171
Variance134.2224431
MonotocityNot monotonic
2021-03-22T13:19:44.029642image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151548
 
3.7%
161545
 
3.6%
171539
 
3.6%
141525
 
3.6%
201503
 
3.5%
181490
 
3.5%
211481
 
3.5%
131478
 
3.5%
121409
 
3.3%
191400
 
3.3%
Other values (73)27432
64.8%
ValueCountFrequency (%)
120
 
< 0.1%
239
 
0.1%
3232
 
0.5%
4476
 
1.1%
5610
1.4%
6750
1.8%
7888
2.1%
81063
2.5%
91141
2.7%
101255
3.0%
ValueCountFrequency (%)
901
< 0.1%
871
< 0.1%
811
< 0.1%
801
< 0.1%
792
< 0.1%
781
< 0.1%
771
< 0.1%
762
< 0.1%
752
< 0.1%
741
< 0.1%

total_pymnt
Real number (ℝ≥0)

HIGH CORRELATION

Distinct42213
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12058.63076
Minimum33.97
Maximum58886.47343
Zeros0
Zeros (%)0.0%
Memory size331.0 KiB
2021-03-22T13:19:44.179637image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum33.97
5-th percentile1850.57112
Q15491.729912
median9725.69238
Q316448.98543
95-th percentile30242.12214
Maximum58886.47343
Range58852.50343
Interquartile range (IQR)10957.25551

Descriptive statistics

Standard deviation9091.066015
Coefficient of variation (CV)0.7539053314
Kurtosis2.175082401
Mean12058.63076
Median Absolute Deviation (MAD)5004.577438
Skewness1.379720177
Sum510683012.5
Variance82647481.3
MonotocityNot monotonic
2021-03-22T13:19:44.310632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12029.456
 
< 0.1%
5614.279443
 
< 0.1%
11784.232233
 
< 0.1%
26687.233
 
< 0.1%
11955.399213
 
< 0.1%
10956.775963
 
< 0.1%
5981.3893243
 
< 0.1%
11553.895943
 
< 0.1%
11804.689323
 
< 0.1%
11437.691453
 
< 0.1%
Other values (42203)42317
99.9%
ValueCountFrequency (%)
33.971
< 0.1%
35.91
< 0.1%
58.031
< 0.1%
66.941
< 0.1%
69.781
< 0.1%
70.071
< 0.1%
76.641
< 0.1%
76.711
< 0.1%
78.971
< 0.1%
83.891
< 0.1%
ValueCountFrequency (%)
58886.473431
< 0.1%
58563.679931
< 0.1%
58480.139921
< 0.1%
58133.31991
< 0.1%
58090.952071
< 0.1%
58071.199821
< 0.1%
58071.199771
< 0.1%
57997.279951
< 0.1%
57835.279911
< 0.1%
57143.259961
< 0.1%

total_pymnt_inv
Real number (ℝ≥0)

HIGH CORRELATION

Distinct39959
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11352.63352
Minimum0
Maximum58563.68
Zeros267
Zeros (%)0.6%
Memory size331.0 KiB
2021-03-22T13:19:44.464628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1194.472
Q14845.9675
median8997.825
Q315578.5025
95-th percentile29671.135
Maximum58563.68
Range58563.68
Interquartile range (IQR)10732.535

Descriptive statistics

Standard deviation9034.480426
Coefficient of variation (CV)0.7958048158
Kurtosis2.220739567
Mean11352.63352
Median Absolute Deviation (MAD)4967.84
Skewness1.393465304
Sum480784029.5
Variance81621836.57
MonotocityNot monotonic
2021-03-22T13:19:44.600623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0267
 
0.6%
6514.5216
 
< 0.1%
5478.3914
 
< 0.1%
13148.1414
 
< 0.1%
6717.9512
 
< 0.1%
11196.5712
 
< 0.1%
10956.7812
 
< 0.1%
13517.3611
 
< 0.1%
7328.9211
 
< 0.1%
5557.0311
 
< 0.1%
Other values (39949)41970
99.1%
ValueCountFrequency (%)
0267
0.6%
0.541
 
< 0.1%
0.921
 
< 0.1%
11.221
 
< 0.1%
12.651
 
< 0.1%
16.821
 
< 0.1%
18.971
 
< 0.1%
20.981
 
< 0.1%
21.61
 
< 0.1%
24.11
 
< 0.1%
ValueCountFrequency (%)
58563.681
< 0.1%
58514.931
< 0.1%
58438.371
< 0.1%
58056.41
< 0.1%
57967.531
< 0.1%
57953.691
< 0.1%
57863.511
< 0.1%
57672.741
< 0.1%
57628.731
< 0.1%
57143.261
< 0.1%

total_rec_prncp
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7669
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9707.626804
Minimum0
Maximum35000.02
Zeros3
Zeros (%)< 0.1%
Memory size331.0 KiB
2021-03-22T13:19:44.746619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1267.178
Q14499.9925
median8000
Q313500
95-th percentile24999.99
Maximum35000.02
Range35000.02
Interquartile range (IQR)9000.0075

Descriptive statistics

Standard deviation7100.759155
Coefficient of variation (CV)0.7314619008
Kurtosis1.143551258
Mean9707.626804
Median Absolute Deviation (MAD)4000
Skewness1.137232723
Sum411117995.1
Variance50420780.57
MonotocityNot monotonic
2021-03-22T13:19:44.879613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100002431
 
5.7%
120001956
 
4.6%
50001853
 
4.4%
60001751
 
4.1%
150001528
 
3.6%
80001411
 
3.3%
200001199
 
2.8%
40001045
 
2.5%
3000969
 
2.3%
7000900
 
2.1%
Other values (7659)27307
64.5%
ValueCountFrequency (%)
03
< 0.1%
21.211
 
< 0.1%
21.931
 
< 0.1%
22.241
 
< 0.1%
22.51
 
< 0.1%
23.681
 
< 0.1%
24.871
 
< 0.1%
30.321
 
< 0.1%
32.511
 
< 0.1%
34.51
 
< 0.1%
ValueCountFrequency (%)
35000.022
 
< 0.1%
35000.011
 
< 0.1%
35000418
1.0%
34999.997
 
< 0.1%
34999.981
 
< 0.1%
34999.971
 
< 0.1%
348001
 
< 0.1%
346751
 
< 0.1%
345251
 
< 0.1%
34475.011
 
< 0.1%

total_rec_int
Real number (ℝ≥0)

Distinct37374
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2247.371068
Minimum3.54
Maximum23886.47
Zeros0
Zeros (%)0.0%
Memory size331.0 KiB
2021-03-22T13:19:45.027622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3.54
5-th percentile190.31
Q1662.68
median1344.735
Q32811.5325
95-th percentile7474.0995
Maximum23886.47
Range23882.93
Interquartile range (IQR)2148.8525

Descriptive statistics

Standard deviation2587.336124
Coefficient of variation (CV)1.151272329
Kurtosis10.0474353
Mean2247.371068
Median Absolute Deviation (MAD)860.025
Skewness2.706185526
Sum95176164.74
Variance6694308.218
MonotocityNot monotonic
2021-03-22T13:19:45.158613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1196.5726
 
0.1%
514.5219
 
< 0.1%
1148.1417
 
< 0.1%
1784.2317
 
< 0.1%
956.7817
 
< 0.1%
717.9517
 
< 0.1%
478.3916
 
< 0.1%
1907.3514
 
< 0.1%
557.0313
 
< 0.1%
1435.913
 
< 0.1%
Other values (37364)42181
99.6%
ValueCountFrequency (%)
3.541
< 0.1%
6.221
< 0.1%
6.271
< 0.1%
7.191
< 0.1%
7.22
< 0.1%
8.061
< 0.1%
8.231
< 0.1%
8.921
< 0.1%
9.341
< 0.1%
9.491
< 0.1%
ValueCountFrequency (%)
23886.471
< 0.1%
23563.681
< 0.1%
23480.141
< 0.1%
23090.951
< 0.1%
23084.931
< 0.1%
23071.22
< 0.1%
22997.281
< 0.1%
22835.281
< 0.1%
22700.391
< 0.1%
22143.261
< 0.1%

total_rec_late_fee
Real number (ℝ≥0)

ZEROS

Distinct2287
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.516277014
Minimum0
Maximum209
Zeros39972
Zeros (%)94.4%
Memory size331.0 KiB
2021-03-22T13:19:45.287609image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile14.97971029
Maximum209
Range209
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.821095463
Coefficient of variation (CV)5.158091424
Kurtosis100.8469328
Mean1.516277014
Median Absolute Deviation (MAD)0
Skewness8.318175938
Sum64214.33155
Variance61.16953425
MonotocityNot monotonic
2021-03-22T13:19:45.411596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
039972
94.4%
1569
 
0.2%
308
 
< 0.1%
453
 
< 0.1%
152
 
< 0.1%
152
 
< 0.1%
152
 
< 0.1%
15.000000012
 
< 0.1%
15.000000012
 
< 0.1%
15.792
 
< 0.1%
Other values (2277)2286
 
5.4%
ValueCountFrequency (%)
039972
94.4%
0.011
 
< 0.1%
0.06079975081
 
< 0.1%
0.07378710411
 
< 0.1%
0.10170456191
 
< 0.1%
0.13999999911
 
< 0.1%
0.18008290351
 
< 0.1%
0.18477362021
 
< 0.1%
0.271
 
< 0.1%
0.30203655331
 
< 0.1%
ValueCountFrequency (%)
2091
< 0.1%
180.21
< 0.1%
170.76000041
< 0.1%
166.42971071
< 0.1%
165.691
< 0.1%
146.60000031
< 0.1%
146.041
< 0.1%
143.09304541
< 0.1%
134.07000071
< 0.1%
130.59703721
< 0.1%

recoveries
Real number (ℝ≥0)

ZEROS

Distinct4998
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean102.1166995
Minimum0
Maximum29623.35
Zeros36077
Zeros (%)85.2%
Memory size331.0 KiB
2021-03-22T13:19:45.542592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile388.5375
Maximum29623.35
Range29623.35
Interquartile range (IQR)0

Descriptive statistics

Standard deviation733.5818909
Coefficient of variation (CV)7.183760292
Kurtosis391.183761
Mean102.1166995
Median Absolute Deviation (MAD)0
Skewness16.67123747
Sum4324642.223
Variance538142.3907
MonotocityNot monotonic
2021-03-22T13:19:45.670587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
036077
85.2%
0.314
 
< 0.1%
1.811
 
< 0.1%
0.9610
 
< 0.1%
1.210
 
< 0.1%
0.99
 
< 0.1%
1.769
 
< 0.1%
0.269
 
< 0.1%
0.729
 
< 0.1%
6.39
 
< 0.1%
Other values (4988)6183
 
14.6%
ValueCountFrequency (%)
036077
85.2%
0.017
 
< 0.1%
0.023
 
< 0.1%
0.034
 
< 0.1%
0.046
 
< 0.1%
0.052
 
< 0.1%
0.068
 
< 0.1%
0.073
 
< 0.1%
0.085
 
< 0.1%
0.095
 
< 0.1%
ValueCountFrequency (%)
29623.351
< 0.1%
277501
< 0.1%
27009.471
< 0.1%
22943.371
< 0.1%
21811.731
< 0.1%
20504.011
< 0.1%
20008.41
< 0.1%
19916.781
< 0.1%
19508.261
< 0.1%
18694.791
< 0.1%

collection_recovery_fee
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct2870
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.2615717
Minimum0
Maximum7002.19
Zeros38100
Zeros (%)90.0%
Memory size331.0 KiB
2021-03-22T13:19:45.809583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.677839998
Maximum7002.19
Range7002.19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation161.8943878
Coefficient of variation (CV)11.35179146
Kurtosis654.6901251
Mean14.2615717
Median Absolute Deviation (MAD)0
Skewness22.55504187
Sum603977.5614
Variance26209.79279
MonotocityNot monotonic
2021-03-22T13:19:45.936579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
038100
90.0%
214
 
< 0.1%
1.211
 
< 0.1%
1.610
 
< 0.1%
1.889
 
< 0.1%
2.029
 
< 0.1%
3.239
 
< 0.1%
1.89
 
< 0.1%
1.449
 
< 0.1%
3.719
 
< 0.1%
Other values (2860)4161
 
9.8%
ValueCountFrequency (%)
038100
90.0%
0.04499999991
 
< 0.1%
0.0631
 
< 0.1%
0.07450000121
 
< 0.1%
0.11811
 
< 0.1%
0.13479999471
 
< 0.1%
0.13930000041
 
< 0.1%
0.141
 
< 0.1%
0.161
 
< 0.1%
0.181
 
< 0.1%
ValueCountFrequency (%)
7002.191
< 0.1%
6972.591
< 0.1%
6543.041
< 0.1%
5774.81
< 0.1%
5602.721
< 0.1%
5569.921
< 0.1%
5216.741
< 0.1%
5192.991
< 0.1%
5036.011
< 0.1%
4902.081
< 0.1%

last_pymnt_d
Categorical

HIGH CARDINALITY

Distinct112
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Mar-2013
 
1068
Dec-2014
 
948
May-2013
 
942
Feb-2013
 
905
Mar-2012
 
892
Other values (107)
37595 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters338800
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowJan-2015
2nd rowApr-2013
3rd rowJun-2014
4th rowJan-2015
5th rowJan-2017
ValueCountFrequency (%)
Mar-20131068
 
2.5%
Dec-2014948
 
2.2%
May-2013942
 
2.2%
Feb-2013905
 
2.1%
Mar-2012892
 
2.1%
Apr-2013890
 
2.1%
Aug-2012866
 
2.0%
Oct-2012852
 
2.0%
Jan-2014844
 
2.0%
Aug-2014835
 
2.0%
Other values (102)33308
78.6%
2021-03-22T13:19:46.216570image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mar-20131068
 
2.5%
dec-2014948
 
2.2%
may-2013942
 
2.2%
feb-2013905
 
2.1%
mar-2012892
 
2.1%
apr-2013890
 
2.1%
aug-2012866
 
2.0%
oct-2012852
 
2.0%
jan-2014844
 
2.0%
aug-2014835
 
2.0%
Other values (102)33308
78.6%

Most occurring characters

ValueCountFrequency (%)
251680
15.3%
146924
13.9%
045899
13.5%
-42350
12.5%
e10766
 
3.2%
a10750
 
3.2%
u10455
 
3.1%
J10142
 
3.0%
39796
 
2.9%
49319
 
2.8%
Other values (23)90719
26.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number169400
50.0%
Lowercase Letter84700
25.0%
Uppercase Letter42350
 
12.5%
Dash Punctuation42350
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
e10766
12.7%
a10750
12.7%
u10455
12.3%
c7558
8.9%
r7480
8.8%
p6809
8.0%
n6545
7.7%
t3655
 
4.3%
l3597
 
4.2%
g3579
 
4.2%
Other values (4)13506
15.9%
ValueCountFrequency (%)
251680
30.5%
146924
27.7%
045899
27.1%
39796
 
5.8%
49319
 
5.5%
52508
 
1.5%
62083
 
1.2%
9831
 
0.5%
8327
 
0.2%
733
 
< 0.1%
ValueCountFrequency (%)
J10142
23.9%
M7484
17.7%
A7003
16.5%
D3903
 
9.2%
O3655
 
8.6%
F3478
 
8.2%
S3385
 
8.0%
N3300
 
7.8%
ValueCountFrequency (%)
-42350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common211750
62.5%
Latin127050
37.5%

Most frequent character per script

ValueCountFrequency (%)
e10766
 
8.5%
a10750
 
8.5%
u10455
 
8.2%
J10142
 
8.0%
c7558
 
5.9%
M7484
 
5.9%
r7480
 
5.9%
A7003
 
5.5%
p6809
 
5.4%
n6545
 
5.2%
Other values (12)42058
33.1%
ValueCountFrequency (%)
251680
24.4%
146924
22.2%
045899
21.7%
-42350
20.0%
39796
 
4.6%
49319
 
4.4%
52508
 
1.2%
62083
 
1.0%
9831
 
0.4%
8327
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII338800
100.0%

Most frequent character per block

ValueCountFrequency (%)
251680
15.3%
146924
13.9%
045899
13.5%
-42350
12.5%
e10766
 
3.2%
a10750
 
3.2%
u10455
 
3.1%
J10142
 
3.0%
39796
 
2.9%
49319
 
2.8%
Other values (23)90719
26.8%

last_pymnt_amnt
Real number (ℝ≥0)

Distinct37205
Distinct (%)87.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2623.355599
Minimum0
Maximum36115.2
Zeros8
Zeros (%)< 0.1%
Memory size331.0 KiB
2021-03-22T13:19:46.339566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.049
Q1213.085
median532.14
Q33190.025
95-th percentile12051.05
Maximum36115.2
Range36115.2
Interquartile range (IQR)2976.94

Descriptive statistics

Standard deviation4391.378244
Coefficient of variation (CV)1.673954628
Kurtosis9.091750621
Mean2623.355599
Median Absolute Deviation (MAD)437.625
Skewness2.743778387
Sum111099109.6
Variance19284202.88
MonotocityNot monotonic
2021-03-22T13:19:46.459562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20019
 
< 0.1%
10017
 
< 0.1%
5017
 
< 0.1%
15013
 
< 0.1%
40012
 
< 0.1%
275.7411
 
< 0.1%
50011
 
< 0.1%
276.069
 
< 0.1%
324.428
 
< 0.1%
08
 
< 0.1%
Other values (37195)42225
99.7%
ValueCountFrequency (%)
08
< 0.1%
0.011
 
< 0.1%
0.021
 
< 0.1%
0.031
 
< 0.1%
0.061
 
< 0.1%
0.131
 
< 0.1%
0.162
 
< 0.1%
0.21
 
< 0.1%
0.221
 
< 0.1%
0.241
 
< 0.1%
ValueCountFrequency (%)
36115.21
< 0.1%
35613.681
< 0.1%
35596.411
< 0.1%
35479.891
< 0.1%
35471.861
< 0.1%
35395.591
< 0.1%
35339.051
< 0.1%
35337.091
< 0.1%
35322.961
< 0.1%
35322.61
< 0.1%

last_credit_pull_d
Categorical

HIGH CARDINALITY

Distinct133
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Aug-2018
9208 
Oct-2016
4016 
Jul-2018
 
1230
May-2018
 
732
Feb-2017
 
707
Other values (128)
26457 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters338800
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowAug-2018
2nd rowOct-2016
3rd rowJun-2017
4th rowApr-2016
5th rowApr-2018
ValueCountFrequency (%)
Aug-20189208
 
21.7%
Oct-20164016
 
9.5%
Jul-20181230
 
2.9%
May-2018732
 
1.7%
Feb-2017707
 
1.7%
Apr-2018597
 
1.4%
Mar-2018593
 
1.4%
Feb-2013563
 
1.3%
Jan-2018546
 
1.3%
Jun-2018531
 
1.3%
Other values (123)23627
55.8%
2021-03-22T13:19:46.729562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aug-20189208
 
21.7%
oct-20164016
 
9.5%
jul-20181230
 
2.9%
may-2018732
 
1.7%
feb-2017707
 
1.7%
apr-2018597
 
1.4%
mar-2018593
 
1.4%
feb-2013563
 
1.3%
jan-2018546
 
1.3%
jun-2018531
 
1.3%
Other values (123)23627
55.8%

Most occurring characters

ValueCountFrequency (%)
245174
13.3%
143827
12.9%
043464
12.8%
-42350
12.5%
u16794
 
5.0%
814015
 
4.1%
A13756
 
4.1%
g11298
 
3.3%
c8149
 
2.4%
a7636
 
2.3%
Other values (23)92337
27.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number169400
50.0%
Lowercase Letter84700
25.0%
Uppercase Letter42350
 
12.5%
Dash Punctuation42350
 
12.5%

Most frequent character per category

ValueCountFrequency (%)
u16794
19.8%
g11298
13.3%
c8149
9.6%
a7636
9.0%
e7420
8.8%
t5880
 
6.9%
r5387
 
6.4%
p4456
 
5.3%
n4411
 
5.2%
l3212
 
3.8%
Other values (4)10057
11.9%
ValueCountFrequency (%)
245174
26.7%
143827
25.9%
043464
25.7%
814015
 
8.3%
67344
 
4.3%
75015
 
3.0%
43966
 
2.3%
33506
 
2.1%
52848
 
1.7%
9241
 
0.1%
ValueCountFrequency (%)
A13756
32.5%
J7623
18.0%
O5880
13.9%
M5509
13.0%
F3153
 
7.4%
D2269
 
5.4%
N2162
 
5.1%
S1998
 
4.7%
ValueCountFrequency (%)
-42350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common211750
62.5%
Latin127050
37.5%

Most frequent character per script

ValueCountFrequency (%)
u16794
13.2%
A13756
 
10.8%
g11298
 
8.9%
c8149
 
6.4%
a7636
 
6.0%
J7623
 
6.0%
e7420
 
5.8%
O5880
 
4.6%
t5880
 
4.6%
M5509
 
4.3%
Other values (12)37105
29.2%
ValueCountFrequency (%)
245174
21.3%
143827
20.7%
043464
20.5%
-42350
20.0%
814015
 
6.6%
67344
 
3.5%
75015
 
2.4%
43966
 
1.9%
33506
 
1.7%
52848
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII338800
100.0%

Most frequent character per block

ValueCountFrequency (%)
245174
13.3%
143827
12.9%
043464
12.8%
-42350
12.5%
u16794
 
5.0%
814015
 
4.1%
A13756
 
4.1%
g11298
 
3.3%
c8149
 
2.4%
a7636
 
2.3%
Other values (23)92337
27.3%

last_fico_range_high
Real number (ℝ≥0)

Distinct72
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean690.2846281
Minimum0
Maximum850
Zeros19
Zeros (%)< 0.1%
Memory size331.0 KiB
2021-03-22T13:19:46.848559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile534
Q1644
median699
Q3749
95-th percentile804
Maximum850
Range850
Interquartile range (IQR)105

Descriptive statistics

Standard deviation80.30934799
Coefficient of variation (CV)0.1163423677
Kurtosis1.973712356
Mean690.2846281
Median Absolute Deviation (MAD)50
Skewness-0.7667598074
Sum29233554
Variance6449.591374
MonotocityNot monotonic
2021-03-22T13:19:46.965547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7091253
 
3.0%
6941215
 
2.9%
7191200
 
2.8%
7241194
 
2.8%
7141172
 
2.8%
7041165
 
2.8%
6991145
 
2.7%
6841113
 
2.6%
6891076
 
2.5%
7341053
 
2.5%
Other values (62)30764
72.6%
ValueCountFrequency (%)
019
 
< 0.1%
499754
1.8%
504210
 
0.5%
509186
 
0.4%
514210
 
0.5%
519212
 
0.5%
524224
 
0.5%
529201
 
0.5%
534222
 
0.5%
539234
 
0.6%
ValueCountFrequency (%)
85010
 
< 0.1%
84432
 
0.1%
83946
 
0.1%
834142
 
0.3%
829190
 
0.4%
824269
0.6%
819365
0.9%
814407
1.0%
809539
1.3%
804618
1.5%

last_fico_range_low
Real number (ℝ≥0)

ZEROS

Distinct71
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean677.4731995
Minimum0
Maximum845
Zeros773
Zeros (%)1.8%
Memory size331.0 KiB
2021-03-22T13:19:47.100543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile530
Q1640
median695
Q3745
95-th percentile800
Maximum845
Range845
Interquartile range (IQR)105

Descriptive statistics

Standard deviation118.7585439
Coefficient of variation (CV)0.1752962981
Kurtosis16.73225549
Mean677.4731995
Median Absolute Deviation (MAD)50
Skewness-3.374302809
Sum28690990
Variance14103.59175
MonotocityNot monotonic
2021-03-22T13:19:47.226538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7051253
 
3.0%
6901215
 
2.9%
7151200
 
2.8%
7201194
 
2.8%
7101172
 
2.8%
7001165
 
2.8%
6951145
 
2.7%
6801113
 
2.6%
6851076
 
2.5%
7301053
 
2.5%
Other values (61)30764
72.6%
ValueCountFrequency (%)
0773
1.8%
500210
 
0.5%
505186
 
0.4%
510210
 
0.5%
515212
 
0.5%
520224
 
0.5%
525201
 
0.5%
530222
 
0.5%
535234
 
0.6%
540256
 
0.6%
ValueCountFrequency (%)
84510
 
< 0.1%
84032
 
0.1%
83546
 
0.1%
830142
 
0.3%
825190
 
0.4%
820269
0.6%
815365
0.9%
810407
1.0%
805539
1.3%
800618
1.5%

acc_now_delinq
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0.0
42346 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters127050
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.042346
> 99.9%
1.04
 
< 0.1%
2021-03-22T13:19:47.453531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-22T13:19:47.529529image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.042346
> 99.9%
1.04
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
084696
66.7%
.42350
33.3%
14
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number84700
66.7%
Other Punctuation42350
33.3%

Most frequent character per category

ValueCountFrequency (%)
084696
> 99.9%
14
 
< 0.1%
ValueCountFrequency (%)
.42350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common127050
100.0%

Most frequent character per script

ValueCountFrequency (%)
084696
66.7%
.42350
33.3%
14
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII127050
100.0%

Most frequent character per block

ValueCountFrequency (%)
084696
66.7%
.42350
33.3%
14
 
< 0.1%

delinq_amnt
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0.0
42348 
27.0
 
1
6053.0
 
1

Length

Max length6
Median length3
Mean length3.000094451
Min length3

Characters and Unicode

Total characters127054
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.042348
> 99.9%
27.01
 
< 0.1%
6053.01
 
< 0.1%
2021-03-22T13:19:47.732521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-22T13:19:47.814528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.042348
> 99.9%
6053.01
 
< 0.1%
27.01
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
084699
66.7%
.42350
33.3%
21
 
< 0.1%
71
 
< 0.1%
61
 
< 0.1%
51
 
< 0.1%
31
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number84704
66.7%
Other Punctuation42350
33.3%

Most frequent character per category

ValueCountFrequency (%)
084699
> 99.9%
21
 
< 0.1%
71
 
< 0.1%
61
 
< 0.1%
51
 
< 0.1%
31
 
< 0.1%
ValueCountFrequency (%)
.42350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common127054
100.0%

Most frequent character per script

ValueCountFrequency (%)
084699
66.7%
.42350
33.3%
21
 
< 0.1%
71
 
< 0.1%
61
 
< 0.1%
51
 
< 0.1%
31
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII127054
100.0%

Most frequent character per block

ValueCountFrequency (%)
084699
66.7%
.42350
33.3%
21
 
< 0.1%
71
 
< 0.1%
61
 
< 0.1%
51
 
< 0.1%
31
 
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0.0
39175 
1.0
 
1838
0.045227107116832636
 
1329
2.0
 
8

Length

Max length20
Median length3
Mean length3.533482881
Min length3

Characters and Unicode

Total characters149643
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.039175
92.5%
1.01838
 
4.3%
0.0452271071168326361329
 
3.1%
2.08
 
< 0.1%
2021-03-22T13:19:48.037520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-22T13:19:48.121518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
0.039175
92.5%
1.01838
 
4.3%
0.0452271071168326361329
 
3.1%
2.08
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
084183
56.3%
.42350
28.3%
15825
 
3.9%
23995
 
2.7%
63987
 
2.7%
72658
 
1.8%
32658
 
1.8%
41329
 
0.9%
51329
 
0.9%
81329
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number107293
71.7%
Other Punctuation42350
 
28.3%

Most frequent character per category

ValueCountFrequency (%)
084183
78.5%
15825
 
5.4%
23995
 
3.7%
63987
 
3.7%
72658
 
2.5%
32658
 
2.5%
41329
 
1.2%
51329
 
1.2%
81329
 
1.2%
ValueCountFrequency (%)
.42350
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common149643
100.0%

Most frequent character per script

ValueCountFrequency (%)
084183
56.3%
.42350
28.3%
15825
 
3.9%
23995
 
2.7%
63987
 
2.7%
72658
 
1.8%
32658
 
1.8%
41329
 
0.9%
51329
 
0.9%
81329
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII149643
100.0%

Most frequent character per block

ValueCountFrequency (%)
084183
56.3%
.42350
28.3%
15825
 
3.9%
23995
 
2.7%
63987
 
2.7%
72658
 
1.8%
32658
 
1.8%
41329
 
0.9%
51329
 
0.9%
81329
 
0.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.5 KiB
False
42190 
True
 
160
ValueCountFrequency (%)
False42190
99.6%
True160
 
0.4%
2021-03-22T13:19:48.190516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

term_new
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
36
31374 
60
10976 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters84700
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row36
2nd row60
3rd row36
4th row36
5th row60
ValueCountFrequency (%)
3631374
74.1%
6010976
 
25.9%
2021-03-22T13:19:48.402499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-22T13:19:48.475496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
3631374
74.1%
6010976
 
25.9%

Most occurring characters

ValueCountFrequency (%)
642350
50.0%
331374
37.0%
010976
 
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number84700
100.0%

Most frequent character per category

ValueCountFrequency (%)
642350
50.0%
331374
37.0%
010976
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
Common84700
100.0%

Most frequent character per script

ValueCountFrequency (%)
642350
50.0%
331374
37.0%
010976
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII84700
100.0%

Most frequent character per block

ValueCountFrequency (%)
642350
50.0%
331374
37.0%
010976
 
13.0%

grade_new
Real number (ℝ≥0)

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.667910272
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Memory size331.0 KiB
2021-03-22T13:19:48.536505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.437641687
Coefficient of variation (CV)0.5388643324
Kurtosis-0.02850051454
Mean2.667910272
Median Absolute Deviation (MAD)1
Skewness0.7578007769
Sum112986
Variance2.066813622
MonotocityNot monotonic
2021-03-22T13:19:48.619502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
212355
29.2%
110163
24.0%
38689
20.5%
45975
14.1%
53369
 
8.0%
61292
 
3.1%
7507
 
1.2%
ValueCountFrequency (%)
110163
24.0%
212355
29.2%
38689
20.5%
45975
14.1%
53369
 
8.0%
61292
 
3.1%
7507
 
1.2%
ValueCountFrequency (%)
7507
 
1.2%
61292
 
3.1%
53369
 
8.0%
45975
14.1%
38689
20.5%
212355
29.2%
110163
24.0%

emp_length_new
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.061180638
Minimum0
Maximum10
Zeros5017
Zeros (%)11.8%
Memory size331.0 KiB
2021-03-22T13:19:48.720498image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q39
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.613099398
Coefficient of variation (CV)0.7138846954
Kurtosis-1.413450655
Mean5.061180638
Median Absolute Deviation (MAD)3
Skewness0.16484189
Sum214341
Variance13.05448726
MonotocityNot monotonic
2021-03-22T13:19:48.817495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1010456
24.7%
05017
11.8%
24728
11.2%
34351
10.3%
43631
 
8.6%
13571
 
8.4%
53441
 
8.1%
62366
 
5.6%
71870
 
4.4%
81585
 
3.7%
ValueCountFrequency (%)
05017
11.8%
13571
8.4%
24728
11.2%
34351
10.3%
43631
8.6%
53441
8.1%
62366
5.6%
71870
 
4.4%
81585
 
3.7%
91334
 
3.1%
ValueCountFrequency (%)
1010456
24.7%
91334
 
3.1%
81585
 
3.7%
71870
 
4.4%
62366
 
5.6%
53441
 
8.1%
43631
 
8.6%
34351
10.3%
24728
11.2%
13571
 
8.4%

home_ownership_new
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
1
20060 
2
18917 
3
3235 
4
 
134
5
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters42350
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
120060
47.4%
218917
44.7%
33235
 
7.6%
4134
 
0.3%
54
 
< 0.1%
2021-03-22T13:19:49.061479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-22T13:19:49.139477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
120060
47.4%
218917
44.7%
33235
 
7.6%
4134
 
0.3%
54
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
120060
47.4%
218917
44.7%
33235
 
7.6%
4134
 
0.3%
54
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number42350
100.0%

Most frequent character per category

ValueCountFrequency (%)
120060
47.4%
218917
44.7%
33235
 
7.6%
4134
 
0.3%
54
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common42350
100.0%

Most frequent character per script

ValueCountFrequency (%)
120060
47.4%
218917
44.7%
33235
 
7.6%
4134
 
0.3%
54
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII42350
100.0%

Most frequent character per block

ValueCountFrequency (%)
120060
47.4%
218917
44.7%
33235
 
7.6%
4134
 
0.3%
54
 
< 0.1%

verification_status_new
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
0
18640 
1
13434 
2
10276 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters42350
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row0
4th row2
5th row2
ValueCountFrequency (%)
018640
44.0%
113434
31.7%
210276
24.3%
2021-03-22T13:19:49.358468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-22T13:19:49.433475image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
018640
44.0%
113434
31.7%
210276
24.3%

Most occurring characters

ValueCountFrequency (%)
018640
44.0%
113434
31.7%
210276
24.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number42350
100.0%

Most frequent character per category

ValueCountFrequency (%)
018640
44.0%
113434
31.7%
210276
24.3%

Most occurring scripts

ValueCountFrequency (%)
Common42350
100.0%

Most frequent character per script

ValueCountFrequency (%)
018640
44.0%
113434
31.7%
210276
24.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII42350
100.0%

Most frequent character per block

ValueCountFrequency (%)
018640
44.0%
113434
31.7%
210276
24.3%

loan_status_new
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.3 MiB
1
36024 
0
6326 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters42350
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1
ValueCountFrequency (%)
136024
85.1%
06326
 
14.9%
2021-03-22T13:19:49.639461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-22T13:19:49.715458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
136024
85.1%
06326
 
14.9%

Most occurring characters

ValueCountFrequency (%)
136024
85.1%
06326
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number42350
100.0%

Most frequent character per category

ValueCountFrequency (%)
136024
85.1%
06326
 
14.9%

Most occurring scripts

ValueCountFrequency (%)
Common42350
100.0%

Most frequent character per script

ValueCountFrequency (%)
136024
85.1%
06326
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII42350
100.0%

Most frequent character per block

ValueCountFrequency (%)
136024
85.1%
06326
 
14.9%

int_rate_new
Real number (ℝ≥0)

HIGH CORRELATION

Distinct394
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1215869705
Minimum0.0542
Maximum0.2459
Zeros0
Zeros (%)0.0%
Memory size331.0 KiB
2021-03-22T13:19:49.807455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.0542
5-th percentile0.0654
Q10.0962
median0.1199
Q30.1472
95-th percentile0.1862
Maximum0.2459
Range0.1917
Interquartile range (IQR)0.051

Descriptive statistics

Standard deviation0.03707854681
Coefficient of variation (CV)0.3049549361
Kurtosis-0.4740132898
Mean0.1215869705
Median Absolute Deviation (MAD)0.0266
Skewness0.240278209
Sum5149.2082
Variance0.001374818634
MonotocityNot monotonic
2021-03-22T13:19:49.952451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1099970
 
2.3%
0.1149831
 
2.0%
0.1349830
 
2.0%
0.0751786
 
1.9%
0.0788742
 
1.8%
0.0749656
 
1.5%
0.1171609
 
1.4%
0.0999607
 
1.4%
0.079582
 
1.4%
0.0542573
 
1.4%
Other values (384)35164
83.0%
ValueCountFrequency (%)
0.0542573
1.4%
0.0579410
1.0%
0.0599346
0.8%
0.0619
 
< 0.1%
0.0603447
1.1%
0.0617252
0.6%
0.063958
 
0.1%
0.0654307
0.7%
0.0662396
0.9%
0.0676168
 
0.4%
ValueCountFrequency (%)
0.24591
 
< 0.1%
0.2441
 
< 0.1%
0.24113
 
< 0.1%
0.239111
< 0.1%
0.23594
 
< 0.1%
0.23529
< 0.1%
0.23229
< 0.1%
0.23139
< 0.1%
0.22942
 
< 0.1%
0.22858
< 0.1%

Interactions

2021-03-22T13:17:48.016010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:48.161335image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:48.288332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:48.512334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:48.637322image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:48.768316image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:48.887321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:49.019837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:49.144897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:49.254900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:49.379895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:49.490876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:49.606882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:49.730883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:49.845384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:49.969347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:50.102342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:50.228352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:50.343336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:50.457332image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:50.582257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:50.708238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:50.834247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:50.951244image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:51.065240image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:51.185236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:51.302228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:17:51.432216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-03-22T13:19:14.638975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-22T13:19:14.764958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
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2021-03-22T13:19:27.516076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-22T13:19:50.135453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-22T13:19:50.580429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-22T13:19:51.031415image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-22T13:19:51.502399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-22T13:19:52.009383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-22T13:19:28.085057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-22T13:19:32.876933image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Unnamed: 0loan_amntfunded_amntfunded_amnt_invtermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statusdescpurposetitlezip_codeaddr_statedtidelinq_2yrsearliest_cr_linefico_range_lowfico_range_highinq_last_6mthsopen_accpub_recrevol_balrevol_utiltotal_acctotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_inttotal_rec_late_feerecoveriescollection_recovery_feelast_pymnt_dlast_pymnt_amntlast_credit_pull_dlast_fico_range_highlast_fico_range_lowacc_now_delinqdelinq_amntpub_rec_bankruptciesdebt_settlement_flagterm_newgrade_newemp_length_newhome_ownership_newverification_status_newloan_status_newint_rate_new
005000.05000.04975.036 months10.65%162.87BB2US Army10+ yearsRENT24000.0VerifiedDec-2011Fully PaidBorrower added on 12/22/11 > I need to upgrade my business technologies.<br>credit_cardComputer860xxAZ27.650.0Jan-1985735.0739.01.03.00.013648.083.7%9.05863.1551875833.845000.00863.160.000.000.00Jan-2015171.62Aug-2018739.0735.00.00.00.0N362101110.1065
112500.02500.02500.060 months15.27%59.83CC4Ryder< 1 yearRENT30000.0Source VerifiedDec-2011Charged OffBorrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike. I only need this money because the deal im looking at is to good to pass up.<br><br> Borrower added on 12/22/11 > I plan to use this money to finance the motorcycle i am looking at. I plan to have it paid off as soon as possible/when i sell my old bike.I only need this money because the deal im looking at is to good to pass up. I have finished college with an associates degree in business and its takingmeplaces<br>carbike309xxGA1.000.0Apr-1999740.0744.05.03.00.01687.09.4%4.01014.5300001014.53456.46435.170.00122.901.11Apr-2013119.66Oct-2016499.00.00.00.00.0N60301200.1527
222400.02400.02400.036 months15.96%84.33CC5US Army10+ yearsRENT12252.0Not VerifiedDec-2011Fully Paidsmall_businessreal estate business606xxIL8.720.0Nov-2001735.0739.02.02.00.02956.098.5%10.03005.6668443005.672400.00605.670.000.000.00Jun-2014649.91Jun-2017739.0735.00.00.00.0N363101010.1596
3310000.010000.010000.036 months13.49%339.31CC1AIR RESOURCES BOARD10+ yearsRENT49200.0Source VerifiedDec-2011Fully PaidBorrower added on 12/21/11 > to pay for property tax (borrow from friend, need to pay back) & central A/C need to be replace. I'm very sorry to let my loan expired last time.<br>otherpersonel917xxCA20.000.0Feb-1996690.0694.01.010.00.05598.021%37.012231.89000012231.8910000.002214.9216.970.000.00Jan-2015357.48Apr-2016604.0600.00.00.00.0N363101210.1349
443000.03000.03000.060 months12.69%67.79BB5University Medical Group1 yearRENT80000.0Source VerifiedDec-2011Fully PaidBorrower added on 12/21/11 > I plan on combining three large interest bills together and freeing up some extra each month to pay toward other bills. I've always been a good payor but have found myself needing to make adjustments to my budget due to a medical scare. My job is very stable, I love it.<br>otherPersonal972xxOR17.940.0Jan-1996695.0699.00.015.00.027783.053.9%38.04066.9081614066.913000.001066.910.000.000.00Jan-201767.30Apr-2018684.0680.00.00.00.0N60211210.1269
555000.05000.05000.036 months7.90%156.46AA4Veolia Transportaton3 yearsRENT36000.0Source VerifiedDec-2011Fully PaidweddingMy wedding loan I promise to pay back852xxAZ11.200.0Nov-2004730.0734.03.09.00.07963.028.3%12.05632.2100005632.215000.00632.210.000.000.00Jan-2015161.03Feb-2017564.0560.00.00.00.0N36131210.0790
667000.07000.07000.060 months15.96%170.08CC5Southern Star Photography8 yearsRENT47004.0Not VerifiedDec-2011Fully PaidBorrower added on 12/18/11 > I am planning on using the funds to pay off two retail credit cards with 24.99% interest rates, as well as a major bank credit card with a 18.99% rate. I pay all my bills on time, looking for a lower combined payment and lower monthly payment.<br>debt_consolidationLoan280xxNC23.510.0Jul-2005690.0694.01.07.00.017726.085.6%11.010137.84000810137.847000.003137.840.000.000.00May-20161313.76Sep-2016654.0650.00.00.00.0N60381010.1596
773000.03000.03000.036 months18.64%109.43EE1MKC Accounting9 yearsRENT48000.0Source VerifiedDec-2011Fully PaidBorrower added on 12/16/11 > Downpayment for a car.<br>carCar Downpayment900xxCA5.350.0Jan-2007660.0664.02.04.00.08221.087.5%4.03939.1352943939.143000.00939.140.000.000.00Jan-2015111.34Dec-2014689.0685.00.00.00.0N36591210.1864
885600.05600.05600.060 months21.28%152.39FF2US Army4 yearsOWN40000.0Source VerifiedDec-2011Charged OffBorrower added on 12/21/11 > I own a small home-based judgment collection business. I have 5 years experience collecting debts. I am now going from a home office to a small office. I also plan to buy a small debt portfolio (eg. $10K for $1M of debt) <br>My score is not A+ because I own my home and have no mortgage.<br>small_businessExpand Business & Buy Debt Portfolio958xxCA5.550.0Apr-2004675.0679.02.011.00.05210.032.6%13.0647.500000647.50162.02294.940.00190.542.09Apr-2012152.39Oct-2016499.00.00.00.00.0N60643200.2128
995375.05375.05350.060 months12.69%121.45BB5Starbucks< 1 yearRENT15000.0VerifiedDec-2011Charged OffBorrower added on 12/16/11 > I'm trying to build up my credit history. I live with my brother and have no car payment or credit cards. I am in community college and work full time. Im going to use the money to make some repairs around the house and get some maintenance done on my car.<br><br> Borrower added on 12/20/11 > $1000 down only $4375 to go. Thanks to everyone that invested so far, looking forward to surprising my brother with the fixes around the house.<br>otherBuilding my credit history.774xxTX18.080.0Sep-2004725.0729.00.02.00.09279.036.5%3.01484.5900001477.70673.48533.420.00277.692.52Nov-2012121.45Dec-2016504.0500.00.00.00.0N60201100.1269

Last rows

Unnamed: 0loan_amntfunded_amntfunded_amnt_invtermint_rateinstallmentgradesub_gradeemp_titleemp_lengthhome_ownershipannual_incverification_statusissue_dloan_statusdescpurposetitlezip_codeaddr_statedtidelinq_2yrsearliest_cr_linefico_range_lowfico_range_highinq_last_6mthsopen_accpub_recrevol_balrevol_utiltotal_acctotal_pymnttotal_pymnt_invtotal_rec_prncptotal_rec_inttotal_rec_late_feerecoveriescollection_recovery_feelast_pymnt_dlast_pymnt_amntlast_credit_pull_dlast_fico_range_highlast_fico_range_lowacc_now_delinqdelinq_amntpub_rec_bankruptciesdebt_settlement_flagterm_newgrade_newemp_length_newhome_ownership_newverification_status_newloan_status_newint_rate_new
423404250310500.04000.01150.036 months16.91%142.43GG2VUTEC, CORP4 yearsRENT68000.0Not VerifiedJul-2007Does not meet the credit policy. Status:Fully Paidi need refi and consolidate my debtscredit_cardrefi333xxFL19.621.0Dec-2001655.0659.03.07.00.01943.025.6%11.05127.5401961474.204000.001127.540.000.000.00Jul-2010143.43Jul-2010499.00.00.00.00.045227N36741010.1691
423414250410000.010000.01450.036 months10.59%325.46CC2US Army2 yearsMORTGAGE55000.0Not VerifiedJul-2007Does not meet the credit policy. Status:Fully PaidAfter playing the balance transfer game for a few years, I would like to obtain a structured loan with a decent interest rate to pay down credit card debt.credit_cardTired Of Balance Transfer Game820xxWY17.960.0Oct-1998725.0729.05.011.00.06559.024.8%45.010717.4566681554.0310000.00717.460.000.000.00Apr-20088114.58Apr-2016809.0805.00.00.00.045227N36322010.1059
423424250520000.020000.0700.036 months15.01%693.45FF1hf palm corp2 yearsOWN80000.0Not VerifiedJul-2007Does not meet the credit policy. Status:Charged Offrefinancingcredit_cardlending333xxFL3.111.0Sep-1997670.0674.06.05.00.07883.059.3%6.08294.140000289.825191.342435.470.00667.336.68Jun-2008693.44Jan-2009504.0500.00.00.00.045227N36623000.1501
42343425066725.06725.0825.036 months13.12%226.98DD5MCHCP10+ yearsMORTGAGE60000.0Not VerifiedJul-2007Does not meet the credit policy. Status:Charged OffI am wanting to consolidate debt so that I only have one payment to make. I work full time, plus own a small side business. I can make the payments for everything currently, but would like to have one payment and make it simpler.debt_consolidationDebt Consolidation651xxMO13.160.0May-1991670.0674.09.010.01.05513.088.9%32.04211.030000515.673039.641171.300.000.090.00Apr-200942.11Aug-2018564.0560.00.00.00.045227N364102000.1312
42344425072000.02000.01025.036 months12.80%67.20DD4Signs by Tomorrow< 1 yearOWN10000.0Not VerifiedJul-2007Does not meet the credit policy. Status:Fully PaidReorganizing my finances, consolidating credit card debt in order to lower my total expenses and eventually get rid of a couple of my credit cardscredit_cardFinancial reorganization280xxNC3.481.0Apr-2004645.0649.00.02.00.0571.061.9%4.02419.1210431239.802000.00419.120.000.000.00Jul-201068.31Aug-2018719.0715.00.00.00.045227N36403010.1280
42345425086000.06000.01200.036 months13.12%202.51DD5SUNY- ESF2 yearsRENT12000.0Not VerifiedJul-2007Does not meet the credit policy. Status:Fully Paidcredit_cardCredit Card132xxNY4.400.0Dec-2004655.0659.09.015.00.05251.049.3%16.07290.2378111458.056000.001290.240.000.000.00Jul-2010204.65Sep-2012664.0660.00.00.00.045227N36421010.1312
423464251110000.010000.0350.036 months14.70%345.18EE5GA-PCOM1 yearRENT50000.0Not VerifiedJul-2007Does not meet the credit policy. Status:Fully Paideducationalpaying for medical school help300xxGA7.222.0Sep-1999650.0654.00.014.00.010025.085%20.012622.317947441.7810000.002570.5451.780.000.00Aug-20101605.65Aug-2010499.00.00.00.00.045227N36511010.1470
42347425122000.02000.01275.036 months7.12%61.87AA1Tzigane Inc7 yearsMORTGAGE150000.0Not VerifiedJul-2007Does not meet the credit policy. Status:Fully Paiddebt_consolidationMiscellanious068xxCT5.600.0Mar-1984800.0804.00.07.00.0150786.02.2%16.02227.0231841419.732000.00227.020.000.000.00Jul-201063.59Jun-2010809.0805.00.00.00.045227N36172010.0712
42348425136000.06000.0650.036 months10.59%195.28CC2Yale University< 1 yearRENT20000.0Not VerifiedJun-2007Does not meet the credit policy. Status:Fully PaidHi, I am a graduate student working full-time to repay credit card loans incurred for living expenses. A $5000 loan would allow me to consolidate my debt into one fixed monthly rate.debt_consolidationDebt consolidation065xxCT12.900.0Jan-1996695.0699.04.05.00.013660.066%6.07029.871272761.576000.001029.870.000.000.00Jun-2010197.36Oct-2014769.0765.00.00.00.045227N36301010.1059
42349425144400.04400.01400.036 months9.64%141.25BB4Brick Township board of education2 yearsMORTGAGE30000.0Not VerifiedJun-2007Does not meet the credit policy. Status:Fully Paiddebt_consolidationVISA087xxNJ3.720.0Jul-2004695.0699.00.04.00.03493.063.5%5.05084.7248681617.874400.00684.720.000.000.00Jun-2010143.28May-2018549.0545.00.00.00.045227N36222010.0964